Financial institutions are at a pivotal moment. As customer expectations evolve and AI reshapes digital engagement, leaders in marketing, CX, and IT must rethink how they deliver value.

Adobe’s report, “State of Customer Experience in Financial Services in an AI-Driven World,” reveals that only 36% of the customer journey is currently personalized, despite 74% of executives acknowledging rising customer expectations. With transformation already underway, financial leaders face five imperatives that demand immediate action to drive relevance, trust, and growth.

1. Make Personalization More Meaningful

Personalization has long been a strategic focus, but today’s consumers expect more than basic segmentation or name-based greetings. They want real-time, omnichannel interactions that align with their financial goals, life stages, and behaviors.

To meet this demand, financial institutions must evolve from reactive personalization to predictive, intent-driven engagement. This means leveraging AI to anticipate needs, orchestrate journeys, and deliver content that resonates with individual context.

Perficient Adobe-consulting principal Ross Monaghan explains, “We are still dealing with disparate data and slow progression into a customer 360 source of truth view to provide effective personalization at scale. What many firms are overlooking is that this isn’t just a data issue. We’re dealing with both a people and process issue where teams need to adjust their operational process of typical campaign waterfall execution to trigger-based and journey personalization.”

His point underscores that personalization challenges go beyond technology. They require cultural and operational shifts to enable real-time, AI-driven engagement.

2. Redesign the Operating Model Around the Customer

Legacy structures often silo marketing, IT, and operations, creating friction in delivering cohesive customer experiences. To compete in a digital-first world, financial institutions must reorient their operating models around the customer, not the org chart.

This shift requires cross-functional collaboration, agile workflows, and shared KPIs that align teams around customer outcomes. It also demands a culture that embraces experimentation and continuous improvement.

Only 3% of financial services firms are structured around the customer journey, though 19% say it should be the ideal.

3. Build Content for AI-Powered Search

As AI-powered search becomes a primary interface for information discovery, the way content is created and structured must change. Traditional SEO strategies are no longer enough.

Customers now expect intelligent, personalized answers over static search results. To stay visible and trusted, financial institutions must create structured, metadata-rich content that performs in AI-powered environments. Content must reflect experience-expertise-authoritativeness-trustworthiness principles and be both machine-readable and human-relevant. Success depends on building discovery journeys that work across AI interfaces while earning customer confidence in moments that matter.

4. Unify Data and Platforms for Scalable Intelligence

Disconnected data and fragmented platforms limit the ability to generate insights and act on them at scale. To unlock the full potential of AI and automation, financial institutions must unify their data ecosystems.

This means integrating customer, behavioral, transactional, and operational data into a single source of truth that’s accessible across teams and systems. It also involves modernizing MarTech and CX platforms to support real-time decisioning and personalization.

But Ross points out, “Many digital experience and marketing platforms still want to own all data, which is just not realistic, both in reality and cost. The firms that develop their customer source of truth (typically cloud-based data platforms) and signal to other experience or service platforms will be the quickest to marketing execution maturity and success.”

His insight emphasizes that success depends not only on technology integration but also on adopting a federated approach that accelerates marketing execution and operational maturity.

5. Embed Guardrails Into GenAI Execution

As financial institutions explore GenAI use cases, from content generation to customer service automation, governance must be built in from the start. Trust is non-negotiable in financial services, and GenAI introduces new risks around accuracy, bias, and compliance.

Embedding guardrails means establishing clear policies, human-in-the-loop review processes, and robust monitoring systems. It also requires collaboration between legal, compliance, marketing, and IT to ensure responsible innovation.

At Perficient, we use our PACE (Policies, Advocacy, Controls, Enablement) Framework to holistically design tailored operational AI programs that empower business and technical stakeholders to innovate with confidence while mitigating risks and upholding ethical standards.

The Time to Lead is Now

The future of financial services will be defined by how intelligently and responsibly institutions engage in real time. These five imperatives offer a blueprint for action, each one grounded in data, urgency, and opportunity. Leaders who move now will be best positioned to earn trust, drive growth, and lead in the AI-powered era.

Learn About Perficient and Adobe’s Partnership

Are you looking for a partner to help you transform and modernize your technology strategy? Perficient and Adobe bring together deep industry expertise and powerful experience technologies to help financial institutions unify data, orchestrate journeys, and deliver customer-centric experiences that build trust and drive growth.

Get in Touch With Our Experts

I think about the early ATMs now and then. No one knew the “right” way to use them. I imagine a customer in the 1970s standing there, card in hand, squinting at this unfamiliar machine and hoping it would give something back; trying to decide if it really dispensed cash…or just ate cards for sport. That quick panic when the machine pulled the card in is an early version of the same confusion customers feel today in digital banking.

People were not afraid of machines. They were afraid of not understanding what the machine was doing with their money.

Banks solved it by teaching people how to trust the process. They added clear instructions, trained staff to guide customers, and repeated the same steps until the unfamiliar felt intuitive. 

However, the stakes and complexity are much higher now, and AI for financial product transparency is becoming essential to an optimized banking UX.

Today’s banking customer must navigate automated underwriting, digital identity checks, algorithmic risk models, hybrid blockchain components, and disclosures written in a language most people never use. Meanwhile, the average person is still struggling with basic money concepts.

FINRA reports that only 37% of U.S. adults can answer four out of five financial literacy questions (FINRA Foundation, 2022).

Pew Research finds that only about half of Americans understand key concepts like inflation and interest (Pew Research Center, 2024).

Financial institutions are starting to realize that clarity is not a content task or a customer service perk. It is structural. It affects conversion, compliance, risk, and trust. It shapes the entire digital experience. And AI is accelerating the pressure to treat clarity as infrastructure.

When customers don’t understand, they don’t convert. When they feel unsure, they abandon the flow. 

How AI is Improving UX in Banking (And Why Institutions Need it Now)

Financial institutions often assume customers will “figure it out.” They will Google a term, reread a disclosure, or call support if something is unclear. In reality, most customers simply exit the flow.

The CFPB shows that lower financial literacy leads to more mistakes, higher confusion, and weaker decision-making (CFPB, 2019). And when that confusion arises during a digital journey, customers quietly leave without resolving their questions.

This means every abandoned application costs money. Every misinterpreted term creates operational drag. Every unclear disclosure becomes a compliance liability. Institutions consistently point to misunderstanding as a major driver of complaints, errors, and churn (Lusardi et al., 2020).

Sometimes it feels like the industry built the digital bank faster than it built the explanation for it.

Where AI Makes the Difference

Many discussions about AI in financial services focus on automation or chatbots, but the real opportunity lies in real-time clarity. Clarity that improves financial product transparency and streamlines customer experience without creating extra steps.

In-context Explanations That Improve Understanding

Research in educational psychology shows people learn best when information appears the moment they need it. Mayer (2019) demonstrates that in-context explanations significantly boost comprehension. Instead of leaving the app to search unfamiliar terms, customers receive a clear, human explanation on the spot.

Consistency Across Channels

Language in banking is surprisingly inconsistent. Apps, websites, advisors, and support teams all use slightly different terms. Capgemini identifies cross-channel inconsistency as a major cause of digital frustration (Capgemini, 2023). A unified AI knowledge layer solves this by standardizing definitions across the system.

Predictive Clarity Powered by Behavioral Insight

Patterns like hesitation, backtracking, rapid clicking, or form abandonment often signal confusion. Behavioral economists note these patterns can predict drop-off before it happens (Loibl et al., 2021). AI can flag these friction points and help institutions fix them.

24/7 Clarity, Not 9–5 Support

Accenture reports that most digital banking interactions now occur outside of business hours (Accenture, 2023). AI allows institutions to provide accurate, transparent explanations anytime, without relying solely on support teams.

At its core, AI doesn’t simplify financial products. It translates them.

What Strong AI-Powered Customer Experience Looks Like

Onboarding that Explains Itself

• Mortgage flows with one-sentence escrow definitions.
• Credit card applications with visual explanations of usage.
  
• Hybrid products that show exactly what blockchain is doing behind the scenes. The CFPB shows that simpler, clearer formats directly improve decision quality (CFPB, 2020).

A Unified Dictionary Across Channels

The Federal Reserve emphasizes the importance of consistent terminology to help consumers make informed decisions (Federal Reserve Board, 2021). Some institutions now maintain a centralized term library that powers their entire ecosystem, creating a cohesive experience instead of fragmented messaging.

Personalization Based on User Behavior

Educational nudges, simplified paths, multilingual explanations. Research shows these interventions boost customer confidence (Kozup & Hogarth, 2008). 

Transparent Explanations for Hybrid or Blockchain-backed Products

Customers adopt new technology faster when they understand the mechanics behind it (University of Cambridge, 2021). AI can make complex automation and decentralized components understandable.

The Urgent Responsibilities That Come With This

GenAI can mislead customers without strong data governance and oversight. Poor training data, inconsistent terminology, or unmonitored AI systems create clarity gaps. That’s a problem because those gaps can become compliance issues. The Financial Stability Oversight Council warns that unmanaged AI introduces systemic risk (FSOC, 2023). The CFPB also emphasizes the need for compliant, accurate AI-generated content (CFPB, 2024).

Customers are also increasingly wary of data usage and privacy. Pew Research shows growing fear around how financial institutions use personal data (Pew Research Center, 2023). Trust requires transparency.

Clarity without governance is not clarity. It’s noise.

And institutions cannot afford noise.

What Institutions Should Build Right Now

To make clarity foundational to customer experience, financial institutions need to invest in:

• Modern data pipelines to improve accuracy
• Consistent terminology and UX layers across channels
• Responsible AI frameworks with human oversight
• Cross-functional collaboration between compliance, design, product, and analytics
• Scalable architecture for automated and decentralized product components
• Human-plus-AI support models that enhance, not replace, advisors

When clarity becomes structural, trust becomes scalable.

Why This Moment Matters

I keep coming back to the ATM because it perfectly shows what happens when technology outruns customer understanding. The machine wasn’t the problem. The knowledge gap was. Financial services are reliving that moment today.

Customers cannot trust what they do not understand.

And institutions cannot scale what customers do not trust.

GenAI gives financial organizations a second chance to rebuild the clarity layer the industry has lacked for decades, and not as marketing. Clarity, in this new landscape, truly is infrastructure.

Related Reading

• The Personalization Gap Is Hurting Financial Services, Here’s How to Close It: A discussion of how personalization shortfalls are holding back financial services from meeting customer expectations. 

• Closing the Gap Between Expectations and Experience in Financial Services: Analysis of rising customer expectations and why legacy systems struggle to keep up. 

• Trust Is the New Currency in Financial Services and Customers Are Setting the Terms: A look at how trust, speed, transparency, and control are reshaping what customers expect from their institutions. 

References 

• Accenture. (2023). Banking top trends 2023. https://www.accenture.com
• Capgemini. (2023). World retail banking report 2023. https://www.capgemini.com
• Consumer Financial Protection Bureau. (2019). Financial well-being in America. https://www.consumerfinance.gov
• Consumer Financial Protection Bureau. (2020). Improving the clarity of mortgage disclosures. https://www.consumerfinance.gov
• Consumer Financial Protection Bureau. (2024). Supervisory highlights: Issue 30. https://www.consumerfinance.gov
• Federal Reserve Board. (2021). Consumers and mobile financial services. https://www.federalreserve.gov
• FINRA Investor Education Foundation. (2022). National financial capability study. https://www.finrafoundation.org
• Financial Stability Oversight Council. (2023). Annual report. https://home.treasury.gov
• Kozup, J., & Hogarth, J. (2008). Financial literacy, public policy, and consumers’ self-protection. Journal of Consumer Affairs, 42(2), 263–270.
• Loibl, C., Grinstein-Weiss, M., & Koeninger, J. (2021). Consumer financial behavior in digital environments. Journal of Economic Psychology, 87, 102438.
• Lusardi, A., Mitchell, O. S., & Oggero, N. (2020). The changing face of financial literacy. University of Pennsylvania, Wharton School.
• Mayer, R. (2019). The Cambridge handbook of multimedia learning. Cambridge University Press.
• Pew Research Center. (2023). Americans and data privacy. https://www.pewresearch.org
• Pew Research Center. (2024). Americans and financial knowledge. https://www.pewresearch.org
• University of Cambridge. (2021). Global blockchain benchmarking study. https://www.jbs.cam.ac.uk

In the rapidly expanding realm of cloud computing, Amazon Web Services (AWS) provides the infrastructure for countless businesses to operate and innovate. But with an ever-increasing amount of data, applications, and workloads on the cloud protecting this data poses significant security challenges. As a firm’s data, applications, and workloads migrate to the cloud, protecting them from both sophisticated threats as well as brute force digital attacks is of paramount importance. This is where Amazon GuardDuty enters as a powerful, vigilant sentinel.

What is Amazon GuardDuty?

At its core, Amazon GuardDuty is a continuous security monitoring service designed to protect your AWS accounts and workloads. The software serves as a 24/7 security guard for your entire AWS environment, not just individual applications, and is constantly scanning for malicious activity and unauthorized behavior.

The software works by analyzing a wide variety of data sources within your firm’s AWS account—including AWS CloudTrail event logs, VPC flow logs, and DNS query logs—using machine learning, threat intelligence feeds, and anomaly detection techniques.

If an external party tries a brute-force login, a compromised instance is communicating with a known malicious IP address, or an unusual API call is made, GuardDuty is there to spot it and can be configured to trigger automated actions through services can trigger automated actions through services like Amazon CloudWatch Events and AWS Lambda when a threat is found as well as alert human administrators to take action.

When a threat is detected, GuardDuty generates a finding with a severity level (high, medium, or low) and a score. The severity and score both help minimize time spent on more routine exceptions while highlighting significant events to your data security team.

Why is GuardDuty So Important?

In today’s digital landscape, relying solely on traditional, static security measures is not sufficient. Cybercriminals are constantly evolving their tactics, which is why GuardDuty is an essential component of your AWS security strategy:

1. Proactive, Intelligent Threat Detection

GuardDuty moves beyond simple rule-based systems. Its use of machine learning allows it to detect anomalies that human security administrators might miss, identifying zero-day threats and subtle changes in behavior that indicate a compromise. It continuously learns and adapts to new threats without requiring manual updates from human security administrators.

2. Near Real-Time Monitoring and Alerting

Speed is critical in incident response. GuardDuty provides findings in near real-time, delivering detailed security alerts directly to the AWS Management Console, Amazon EventBridge, and Amazon Security Hub. This immediate notification allows your firm’s security teams to investigate and remediate potential issues quickly, minimizing potential damage and alerting your firm’s management.

3. Broad Protection Across AWS Services

GuardDuty doesn’t just watch over your firm’s Elastic Compute Cloud (“EC2”) instances. GuardDuty also protects a wide array of AWS services, including:

• Simple Storage Service (“S3”) Buckets: Detecting potential data exfiltration or policy changes that expose sensitive data.
• EKS/Kubernetes: Monitoring for threats to your container workloads.  No more running malware or mining bitcoin in your firm’s containers.
• Databases (Aurora; RDS – MySQL, PostgreSQL, MariaDB, Oracle, and Microsoft SQL Server; and Redshift): Identifying potential compromise or unauthorized access to data.

Conclusion:

In the cloud, security is a shared responsibility. While AWS manages the security of the cloud infrastructure itself, you are responsible for security in the cloud—protecting your data, accounts, and workloads. Amazon GuardDuty is an indispensable tool in fulfilling that responsibility. It provides an automated, intelligent, and scalable layer of defense that empowers you to stay ahead of malicious actors.

To enable Amazon GuardDuty, consider contacting Perficient to help enable, configure, and train staff. Perficient is an AWS partner and has achieved Premier Tier Services Partner status, the highest tier in the Amazon Web Services (AWS) Partner Network. This elevated status reflects Perficient’s expertise, long-term investment, and commitment to delivering customer solutions on AWS.

Besides the firm’s Partner Status, Perficient has demonstrated significant expertise in areas like cloud migration, modernization, and AI-driven solutions, with a large team of AWS-certified professionals.

In addition to these competencies, Perficient has been designated for specific service deliveries, such as AWS Glue Service Delivery, and also has available Amazon-approved software in the AWS Marketplace.

Our financial services experts continuously monitor the financial services landscape and deliver pragmatic, scalable solutions that meet the required mandate and more. Reach out to Perficient’s Director and Head of Payments Practice Amanda Estiverne-Colas to discover why Perficient has been trusted by 18 of the top 20 banks, 16 of the 20 largest wealth and asset management firms, and 25+ leading payment + card processing companies.

The days of measuring marketing success by impressions and engagement are over, especially in financial services. Today, marketing leaders are being asked to do more than build brand awareness. They’re expected to drive top-line growth. 

According to Adobe’s report, “State of Customer Experience in Financial Services in an AI-Driven World,” 90% of financial services marketing leaders say they’re now expected to directly contribute to revenue. And 96% are being asked to become more efficient while doing so. 

This new mandate requires not only a change in metrics but a mindset transformation as well. 

Marketing is Now a Growth Engine

Modern financial institutions are retooling their marketing functions to prioritize: 

• Pipeline creation 

• Product adoption 

• Customer lifetime value

Campaigns are no longer judged by vanity KPIs. Success is measured by conversion lift, wallet share, and ROI. That means marketing must operate with the same precision and accountability as sales and finance. 

Performance-Driven Marketing Requires New Infrastructure

To meet these expectations, marketing teams need: 

• Attribution models that tie spend to outcomes 

• Automation platforms that enable real-time optimization 

• Journey tracking that connects every touchpoint to business impact 

Along with the right tools, financial services marketers will also need to build a culture of continuous improvement and commercial fluency. 

Business Fluency is the New Financial Services Marketing Skillset

To lead in this environment, marketers must speak the language of finance. That means understanding: 

• Unit economics 

• Acquisition cost 

• Profitability metrics 

Winning teams are breaking down silos between marketing, sales, and product to drive aligned, data-informed execution. Financial services marketing is moving beyond a support function to a strategic partner in growth. 

Precision, Accountability, and Impact

By embracing data-driven strategies, building the right infrastructure, and fostering commercial fluency, marketing teams can move from a support function to a strategic driver of revenue. The organizations that succeed will be those that align marketing with business outcomes and lead with precision, accountability, and agility. 

Download the full Adobe report to learn more about the top insights shaping financial services marketing and the industry as a whole. 

How Perficient and Adobe Help Financial Services Marketers Lead

We help financial services firms modernize their marketing operations from journey orchestration to performance measurement. Together with Adobe, we’re enabling marketing teams to become growth architects, not just brand custodians. 

Let’s connect and uncover new ways to drive measurable impact together.

In speaking recently with my current co-worker Amanda Estiverne-Colas, who serves as Director and Head of Payments Practice at Perficient, Amanda shared with me statistics she had provided to her audience at the 2025 GULF AML Forum, an annual conference for anti-money laundering (AML) professionals in the financial services industry and government. The statistics, which I found both fascinating and scary, included: 

• Phishing attacks have surged by 4,151% just since ChatGPT’s launch in 2022 

• Phone Phishing attacks increased by 28% in Q3 2024, while smishing incidents rose by 22% 

• More than half (53%) of all breaches involve customer PII, which can include tax identification numbers, emails, phone numbers, and home addresses 

For the clarity of readers, phishing is the fraudulent practice of sending email messages purporting to be from reputable companies in order to induce individuals to reveal personal information, such as passwords or credit card numbers, and smishing is the fraudulent practice of sending text messages purporting to be from reputable companies in order to induce individuals to reveal personal information, such as passwords or credit card numbers. 

In our hyper-connected world, digital transactions occur at lightning speed, creating a vast and complex landscape for financial crime. While artificial intelligence and machine learning tools are vital in the fight against fraud, the human element remains the cornerstone of effective defense. Fraud detection isn’t just about algorithms; it’s about the people behind the screens—the victims, the fraudsters, the analysts, and the developers. 

As I spoke with Amanda about how financial institutions and consumers can fight against burgeoning fraud, I was reminded of the teaching of a former co-worker from much earlier in my career. Having just finished serving in the army, that co-worker highlighted the motto of the Seven Ps. Those Ps being “Proper Prior Planning Prevents Piss-Poor Performance”. The current, and with all-due respect to the members of our armed forces, better-etiquette, saying is limited to the Four Ps—Protect, Prepare, Pursue, and Prevent. Using this, readers can gain a holistic understanding of how more resilient, human-centric systems can be designed and built to combat fraud. 

Protect: Safeguarding More Than Just Data 

Protection is the primary line of defense, extending beyond a company’s balance sheet to its reputation, customer trust, and employee well-being. In the digital age, this means creating safeguards that are both technologically advanced and human aware. 

The human side of protection involves recognizing that the primary target of many modern fraud schemes is not system vulnerability, but human psychology. Social engineering preys on trust, fear, and urgency. As such, the most crucial protective measure becomes continuous human training and awareness. Staff must be educated in the latest social engineering tactics, red flags in communication, and subtle behavioral changes that might indicate internal fraud, such as an employee living beyond their means or refusing to share job duties. 

Furthermore, dealing with victims of fraud requires a distinct human touch. A customer who has lost their life savings to an online scam needs empathy and clear, supportive guidance, not automated responses. Human analysts serve as the compassionate front line, helping victims navigate a distressing experience and rebuild trust in the institution. 

Prepare: Cultivating Resilience and Expertise 

Preparation means anticipating complexity and ambiguity, as fraudsters constantly adapt their methods. Technology helps, but it is the trained professional who must handle the unexpected. 

A significant human challenge in this phase is managing “alert fatigue”. Advanced fraud detection systems generate high volumes of alerts, many of which are false positives (legitimate transactions incorrectly flagged as fraud). Analysts, overwhelmed by the sheer volume, may become desensitized to actual threats. This is where human expertise and critical thinking are indispensable. Experienced analysts provide essential feedback on the utility of detection models, helping to tune systems to be more accurate and reduce false positives. 

Preparation also involves developing professional resilience. Investigators deal with angry victims and deceptive individuals, requiring emotional intelligence and clear communication skills. The human element in preparation ensures that institutions are not just structurally ready with protocols but also staffed with people who are mentally and skillfully equipped to handle high-stress situations. 

Pursue: The Art of Human Investigation 

When fraud occurs, the pursuit begins. While data analytics help “follow the money,” human investigators are the ones who put the pieces together, often leveraging a combination of technical knowledge and investigative experience. 

Transactions in a digital landscape rarely move in straight lines. Criminals use layering, cross-jurisdictional transfers, and digital assets to obscure the path. Pursuing these requires human ingenuity to connect seemingly unrelated data points and understand the “why” behind the transactions. 

Crucially, pursuit relies heavily on inter-institutional and human collaboration. Sharing information between banks and agencies, often hampered by misinterpretations of privacy laws, is a human-led effort to overcome organizational silos. Human networks and trusted relationships between compliance professionals are essential to disrupt criminal activity effectively. 

Prevent: The Continuous Cycle of Learning 

Prevention is about learning from every case and educating both consumers and staff to stop future occurrences. 

Starting with an all-digital approach, one bank that worked with Amanda and her team initiated real-time transaction notifications requiring instant customer verification, to help prevent fraud. Another financial institution worked with Perficient to modify their in-app fraud education library updated weekly with new threats. AI-powered analysis of customer transaction patterns triggers proactive educational interventions before fraud occurs.  

This final P is not just all-digital but also brings us back to the human element as the loop-closer in the system. Every investigation offers insights into new fraud typologies, compromised onboarding flows, or novel social engineering tactics. It is up to human teams—investigators, risk managers, and product developers—to establish effective feedback loops. 

The human side of prevention is fostering a culture where fraud is not a siloed responsibility but a part of the organization’s DNA. It involves embedding “compliance by design” into new digital products, ensuring that human-centric insights are used to make systems inherently more secure. 

Conclusion: 

Ultimately, the digital age has made fraud detection faster and more data-intensive, but the core battle remains human versus human—the fraudsters’ psychology against the collective ingenuity and integrity of those dedicated to stopping them. By embracing the Four Ps of Protect, Prepare, Pursue, and Prevent, Perficient can combine their AI expertise with technical and compliance staff and ensure that both human and artificial intelligence are combined successfully at the heart of your firm’s defensive strategies against fraud.  

Our financial services experts continuously monitor the financial services landscape and deliver pragmatic, scalable solutions that meet the required mandate and more. Reach out to Perficient’s Director and Head of Payments Practice Amanda Estiverne-Colas to discover why we’ve been trusted by 18 of the top 20 banks, 16 of the 20 largest wealth and asset management firms, and 25+ leading payment + card processing companies and are regularly recognized by leading analyst firms. 

In financial services, trust has always been foundational. But today, it’s being redefined, not by brand reputation or policy language, but by how customers experience speed, control, and transparency in real time. 

According to Adobe’s report, “State of Customer Experience in Financial Services in an AI-Driven World,” 96% of financial services executives say customers value privacy and data protection, and 63% say they expect transparent pricing. These have become operational expectations, and they’re shaping how trust is built moment by moment.

Trust Is Built in the Details Customers Can See

A face-ID login. A real-time transaction alert. A personalized financial nudge. These micro-moments now carry more weight than any static privacy policy. Customers judge trustworthiness by how responsive and secure their digital experiences feel—especially when managing sensitive tasks like wire transfers, credit approvals, or investment decisions. 

In this new landscape, trust is engineered, not assumed. 

Designing for Trust Means Designing for the Customer

Customers today expect more than digital convenience. They want to feel in control of their money, identity, and digital footprint and engage with institutions that respect their time, values, and privacy. Trust is no longer built solely through face-to-face interactions or legacy brand reputation. Trust is earned through every digital touchpoint.

To meet these expectations, financial institutions must deliver on three critical fronts:

1. Mobile-First Journeys With Instant Authentication

Customers expect secure access anytime, anywhere. A mobile-first design can enable frictionless, secure interactions that reinforce a sense of control and safety. Biometric authentication, real-time alerts, and intuitive navigation all contribute to a trustworthy experience.

2. Personalized Recommendations That Reflect Their Financial Goals

Trust grows when customers feel understood. Using AI and data responsibly to deliver tailored insights, whether it’s budgeting tips, investment opportunities, or credit alerts, shows that the institution is aligned with the customer’s financial well-being. Transparency in how data is used is key to maintaining that trust.

3. Seamless, Omnichannel Experiences That Feel Consistent and Secure

Whether a customer is engaging via app, website, call center, or in-branch, the experience should feel unified and secure. Consistency in branding, messaging, and service quality reinforces reliability, while secure data handling across channels ensures peace of mind.

Institutions that fail to deliver these experiences risk losing not just attention but loyalty. In a competitive landscape where switching providers is easier than ever, trust becomes a differentiator and a strategic imperative.

From Compliance Output to Design Input

Trust has become a core design principle. Instead of treating it as the outcome of compliance, financial institutions are embedding it into the very fabric of the customer experience. This shift reflects a broader understanding: trust is emotional, experiential, and earned in moments, not just mandated in policies.

That means:

Aligning products, security, and experience teams.

Trustworthy experiences require collaboration across silos. When product managers, cybersecurity experts, and UX designers work together, they can create solutions that are not only secure but also intuitive and empathetic. This alignment ensures that security features enhance, not hinder, the user experience.

Ensuring Personalization respects boundaries and data use is clearly communicated.

Customers want tailored experiences, but also want to know their data is safe. Leading institutions are adopting privacy-by-design principles, making it easy for users to understand how their data is used and giving them control over personalization settings. Transparency builds confidence; ambiguity erodes it.

Embedding transparency and predictability into every screen and interaction.

From clear language in disclosures to consistent UI patterns, every detail matters. Predictable flows, upfront information, and visible security cues (like encryption badges or session timers) help users feel safe and informed. These micro-moments of clarity add up to a macro-impact on trust.

This evolution requires cross-functional collaboration and a deep understanding of customer expectations.

Ready to Build Trust Through Experience Design?

Download the full Adobe report to explore the top 10 insights shaping the future of financial services, and discover how your organization can lead with intelligence, responsibility, and trust.

Learn About Perficient and Adobe’s Partnership

Perficient and Adobe bring together deep industry expertise and powerful experience technologies to help financial services organizations unify data, orchestrate journeys, and deliver customer-centric experiences that build trust and drive growth.

Get in Touch With Our Experts

We’re excited to announce that Perficient has been named a 2025 Salesforce Partner Innovation Award winner in Financial Services! This recognition highlights our commitment to helping financial institutions modernize with AI, data, and Salesforce-powered experiences.

“The Salesforce 2025 Partner Innovation Awards celebrate the achievements of partners who are redefining industries and delivering exceptional customer experiences with Salesforce.” – Jim Steele, President, Global Strategic Customers and Partners, Salesforce

Why This Award Matters

The Salesforce Partner Innovation Awards celebrate partners who deliver exceptional customer experiences and redefine industries through innovation. As technologies like AI evolve rapidly, partners play a critical role in helping businesses unlock their full potential.

According to IDC:

• Salesforce and its ecosystem — fueled by AI — will create 11.6 million new jobs and more than $2 trillion in business revenues.
• 9 out of 10 Salesforce customers rely on partner apps and expertise.
• 100% of the Fortune 100 have installed a Salesforce partner solution.

Our Winning Story: Perficient + Nationwide

Nationwide wanted to unify siloed customer data without relying on complex, costly integration methods. Using Salesforce Data 360, Perficient helped Nationwide activate advanced segmentation capabilities at scale.

Solution Highlights:

• Leveraged Data 360’s built-in data sharing capabilities
• Ingested millions of rows of data from Databricks, Snowflake, and Marketing Cloud
• Enabled identity resolution across millions of records

Key Benefits:
Marketing, sales, and service teams can now build and activate granular, cross-channel segments that enable more agile, targeted, and proactive customer engagement.

What’s Next?

We’re proud to partner with Salesforce to help financial institutions embrace AI-driven innovation. Ready to learn more? Visit our new Build Your Agentic Enterprise With Salesforce Resource Center to explore how Perficient can help you unlock the power of data and AI.

Financial services leaders are turning our 2025’s top digital payments trends into reality—from leveraging AI for smarter decision-making to embedding finance for seamless customer experiences. Insights from events like Fintech South and Money20/20 reinforce that leading firms are moving fast to make these priorities into real-world strategies. We sat down with Amanda Estiverne, Director – Head of Payments, to explore how organizations are acting on these trends today, and what it means for the future of payments.

Key Trends Driving 2025 Digital Payment Strategies

AI-Driven Payment Innovations With Purpose

The next wave of artificial intelligence (AI) in payments isn’t just about efficiency. It’s about amplifying human creativity and problem‑solving to deliver hyper‑personalized, conversational payment experiences and smarter decisions while meeting rising expectations for safety and compliance.

“We’re living in a pivotal moment where artificial intelligence is no longer just about automating tasks — it’s about amplifying human creativity and problem-solving.”

At Fintech South, this theme came through loud and clear. Leaders explored how AI can empower us—not replace us—to achieve things never thought possible before. This aligns with our previous trend prediction that AI-driven payment innovations would dominate 2025, provided firms balance innovation with compliance and ethics.

Where firms are focusing:

• Personalization at scale: Using GenAI to tailor offers, loyalty, and checkout flows without adding friction.
• Conversational experiences: Voice and chat interfaces that are grounded in robust data privacy and make payments feel frictionless.
• Responsible adoption: Governance, data minimization, and explainability baked into model life cycles, not bolted on later.

Success in Action: Intelligently Mining Complex Content With an LLM Assistant

Embedded Finance for Social Good and Customer Loyalty

Embedded payments are moving beyond convenience to purpose-driven experiences to meet people where they are and close real gaps. We’re seeing momentum in:

• Earned wage access (EWA): Helping hourly and frontline workers improve resilience by accessing earned pay when it’s needed.
• Frictionless giving: Removing steps from donation flows and matching programs so generosity fits naturally into digital journeys.

“What happens when we challenge employers, financial institutions, and fintech innovators — the true system builders — to see themselves not just as service providers, but as architects of opportunity, agents of equity, and accelerators of change?”

Strategic partnerships can move the needle when they pair impact with disciplined risk and compliance. This validates our earlier prediction that embedded finance would expand beyond retail into sectors like healthcare, philanthropy, and payroll.

Where firms are focusing:

• Designing connected experiences that deepen trust and retention.
• Partnering for scale and co-creating with employers, financial institutions, and mission-driven orgs to reach underserved populations.
• Operational rigor to create clear controls for fraud, data sharing, and disclosures as embedded use cases expand.

Explore More: Build a Powerful Connected Products Strategy

Designing Payments for Fairness, Trust, and Compliance

Trust is becoming a design requirement, not a line item. Frameworks like “Fairness by Design” from Consumer Reports underscore principles including transparency, privacy, user-centricity, and financial well-being that are increasingly decisive for adoption and loyalty.

Building for inclusivity and clarity reduces drop-off and disputes while strengthening brand equity. This aligns with our February trend on navigating the regulatory landscape—where compliance and user experience converge.

Where firms are focusing:

• Designing experiences with authentication, reconciliation, and fraud prevention baked in from the first interaction.
• Leveraging advanced analytics and AI to strengthen compliance, minimize false positives, and accelerate dispute resolution.

Success In Action: Ensuring Interoperable, Compliant Real-Time Payments

How These Trends Are Shaping the Future of Payments

Payments are becoming faster, smarter, and more embedded—but also more complex. Based on our previous outlook and what we’re hearing now, here’s where firms are concentrating their investments:

• AI with accountability: Scaling intelligent automation while embedding fairness and explainability.
• Embedded finance with purpose: Turning the support of financial wellbeing and social impact into competitive differentiators.
• Real-time payments with resilience: Moving beyond speed to orchestration, fraud prevention, and liquidity optimization.

“When civic leaders, corporate visionaries, and mission-driven organizations work together, fintech becomes more than technology — it becomes a powerful force for equity, inclusion, and opportunity.”

As McKinsey notes, global payments are entering a “simpler interface, complex reality” era where user experience feels effortless, but the infrastructure behind it demands precision and trust.

You May Also Enjoy: Efi Pylarinou, Top Global Tech Thought Leader On FinTech

Design Responsibly and Innovate Inclusive Payment Experiences

We help payment and fintech firms innovate and boost market position with transformative digital experiences and efficient operations.

• Business Transformation: Create a roadmap to innovate products, enhance experiences, and reduce transactional risk.
• Modernization: Implement technology to improve payment processing, fraud management, and omnichannel experiences.
• Data + Analytics: Proactively leverage integrated data and AI to optimize transactions, manage fraud, and personalize experiences.
• Risk + Compliance: Enhance compliance and risk management to safeguard transactions and customer data.
• Consumer Experience: Deliver convenient, seamless experiences with user-friendly secure payment solutions.

Our approach to designing and implementing AI and machine learning (ML) solutions promotes secure and responsible adoption and ensures demonstrated and sustainable business value.

Discover why we have been trusted by 25+ leading payments and card processing companies. Explore our financial services expertise and contact us to learn more.

Turn on CNBC during almost any trading day and you’ll see and hear plenty of AI buzz that sounds great, and may look great in a deck, but falls short in regulated industries. For financial services firms, AI must do two things at once: unlock genuine business value and satisfy strict compliance, privacy, and audit requirements. Salesforce’s AI stack — led by Einstein GPT, Data Cloud, and integrated with MuleSoft, Slack, and robust security controls — is engineered to meet that dual mandate. Here’s a practical look at what Salesforce AI delivers for banks, insurers, credit unions, wealth managers, and capital markets firms, and how to extract measurable value without trading off controls and/or governance.

What Salesforce AI actually is (and why it matters for Financial Services)

Salesforce is widely adopted by financial services firms, with over 150,000 companies worldwide using its CRM, including a significant portion of the U.S. market, where 83% of businesses opt for its Financial Services Cloud (“FSC”). Major financial institutions like Wells Fargo, Bank of America Merrill Lynch and The Bank of New York are among its users, demonstrating its strong presence within the industry. Salesforce has combined together generative AI, predictive models, and enterprise data plumbing into a single ecosystem. Key capabilities include:

• Einstein GPT: Generative AI tailored for CRM workflows — draft client communications, summarize notes, and surface contextual insights using your internal data.
• Data Cloud: A real-time customer data platform that ingests, unifies, and models customer profiles at scale, enabling AI to operate on a trusted single source of truth.
• Tableau + CRM Analytics: Visualize model outcomes, monitor performance, and create operational dashboards that align AI outputs with business KPIs.
• MuleSoft: Connectors and APIs to bring core banking, trading, and ledger systems into the loop securely.
• Slack & Flow (and Flow Orchestrator): Operationalize AI outputs into workflows, approvals, and human-in-the-loop processes.

For financial services, that integration matters more than flashy demos: accuracy, traceability, and context are non-negotiable. Salesforce’s ecosystem lets you apply AI where it impacts revenue, risk, and customer retention — and keep audit trails for everything.

High-value financial services use cases

Here are the pragmatic use cases where Salesforce AI delivers measurable ROI:

Client advisory and personalization

Generate personalized portfolio reviews, client outreach, or renewal communications using Einstein GPT combined with up-to-date holdings and risk profiles from Data Cloud. The result: more relevant outreach and higher conversion rates with less advisor time.

Wealth management — scalable advice and relationship mining

AI-driven summarization of client meetings, automated risk-tolerance classifiers, and opportunity scoring help advisors prioritize high-value clients and surface cross-sell opportunities without manual data wrangling.

Commercial lending — faster decisioning and better risk controls

Combine predictive credit risk models with document ingestion (via MuleSoft and integrated OCR) to auto-populate loan applications, flag exceptions, and route for human review where model confidence is low.

Fraud, AML, and compliance augmentation

Use real-time customer profiles and anomaly detection to surface suspicious behaviors. AI can triage alerts and summarize evidence for investigators, improving throughput while preserving explainability for regulators. AI can also reduce the volume of false alerts, which is the bane of every compliance officer ever.

Customer support and claims

RAG-enabled virtual assistants (Einstein + Data Cloud) pull from policy language, transaction history, and client notes to answer common questions or auto-draft claims responses — reducing service time and improving consistency. The virtual assistants can also interact in multiple languages, which helps reduce customer turnover for non-English writing clients.

Sales and pipeline acceleration

Predictive lead scoring, propensity-to-buy models, and AI-suggested next-best actions increase win rates and shorten sales cycles. Integrated workflows push suggestions to reps in Slack or the Salesforce console, making adoption frictionless.

Why Salesforce’s integrated approach reduces risk

Financial firms can’t treat AI as a separate experiment. Salesforce’s value proposition is that AI is embedded into systems that already handle customer interactions, security, and governance. That produces the following practical advantages:

Single source of truth

Data Cloud reduces conflicting customer records and stale insights, which directly lowers the risk of AI producing inappropriate or inaccurate outputs.

Controlled model access and hosting options

Enterprises can choose where data and model inference occur, including private or managed-cloud options, helping meet residency and confidentiality requirements.

Explainability and audit trails

Salesforce logs user interactions, AI-generated outputs, and data lineage into the platform. That creates the documentation regulators ask for and lets financial services executives investigate where models made decisions.

Human-in-the-loop and confidence thresholds

Workflows can be configured so that high-risk or low-confidence outputs require human approval. That’s essential for credit decisions, compliance actions, and investment advice.

Implementation considerations for regulated firms

To assist in your planned deployment of Salesforce AI in financial services, here’s a checklist of practical guardrails and steps:

Start with business outcomes, not models

• Identify high-frequency, low-risk tasks for pilots (e.g., document summarization, inquiry triage) and measure lift on KPIs like turnaround time, containment rate, and advisor productivity.

Clean and govern your data

Invest in customer identity resolution, canonicalization, and metadata tagging in Data Cloud. Garbage in, garbage out is especially painful when compliance hangs on a model’s output.

Create conservative guardrails

Hard-block actions that have material customer impact (e.g., account closure, fund transfers) from automated flows. Use AI to assist drafting and recommendation, not to execute high-risk transactions autonomously.

Establish model testing and monitoring

Implement A/B tests, accuracy benchmarks, and drift detection. Integrate monitoring into Tableau dashboards and set alerts for performance degradation or unusual patterns.

Document everything for auditors and regulators

Maintain clear logs of training data sources, prompt templates, model versions, and human overrides. Salesforce’s native logging plus orchestration records from Flow help with this.

Train users and change-manage

Advisors, compliance officers, and client service reps should be part of prompt tuning and feedback loops. Incentivize flagging bad outputs — their corrections will dramatically improve model behavior.

Measurable outcomes to expect

When implemented with discipline, financial services firms typically see improvements including:

• Reduced average handling time and faster loan turnaround
• Higher client engagement and improved cross-sell conversion
• Fewer false positives and faster investigator resolution times
• Better advisor productivity via automated notes and suggested actions

Those outcomes translate into cost savings, improved regulatory posture, and revenue lift — the hard metrics CFOs, CROs, and CCOs require.

Final thoughts — pragmatic AI adoption

Salesforce gives financial institutions a practical path to embed AI into customer-facing and operational workflows without ripping up existing systems. The power isn’t just in the model; it’s in the combination of unified data (Data Cloud), generative assistance (Einstein GPT), secure connectors (MuleSoft), and operationalization (Flows and Slack). If you treat governance, monitoring, and human oversight as first-class citizens, AI becomes an accelerant — not a liability.

To help financial services firms either install or expand their Salesforce capability, Perficient has a 360-degree strategic partnership with Salesforce. While Salesforce itself is the provider of the platform and technology, as a global digital consultancy firm Perficient partners with Salesforce to offer its expertise in implementation, customization, and optimization of Salesforce solutions, leveraging Salesforce’s AI-first technologies and platform to deliver consulting, implementation, and integration services. Working together, Salesforce and Perficient’s partnership helps mutual clients build customer-centric solutions and operate as “agentic enterprises”.  

In today’s financial landscape, personalization is no longer a luxury; it’s a customer expectation. Yet, according to Adobe’s latest State of Customer Experience in Financial Services in an AI-Driven World report, only 36% of the customer journey is currently personalized, despite 74% of financial services executives acknowledging that their customers expect tailored interactions.

This gap isn’t just a missed opportunity; it’s a trust breaker.

Why Personalization Matters More Than Ever

Financial decisions are deeply personal. Whether a customer is exploring mortgage options, planning for retirement, or managing small business finances, they expect advice and experiences that reflect their unique goals and life stage. Generic nudges and one-size-fits-all messaging simply don’t cut it anymore.

Early-stage interactions—like product discovery or financial education—are especially critical. These are high-value moments where relevance builds trust and guides decision-making. Yet many institutions fall short, lacking the orchestration needed to deliver personalized engagement across these initial touchpoints.

What’s Holding Institutions Back?

The report highlights several barriers:

• Fragmented data systems that prevent a unified view of the customer
• Legacy operating models that prioritize product silos over customer journeys
• Compliance concerns that limit personalization efforts, even when customers expect it

These challenges are compounded by the rise of AI-powered experiences, which demand real-time, context-aware personalization across channels.

The Path Forward: Adaptive, Lifecycle Personalization

To close the gap, financial institutions must evolve from episodic personalization to adaptive, full-lifecycle engagement. That means:

• Investing in unified customer profiles and behavioral insights
• Building real-time content engines that respond to customer signals
• Designing personalization strategies that grow with the relationship and not just the transaction

Download the full Adobe report to explore the top 10 insights shaping the future of financial services, and discover how your organization can lead with intelligence, responsibility, and trust.

Learn About Perficient and Adobe’s Partnership

Are you looking for a partner to help you transform and modernize your technology strategy? Perficient and Adobe bring together deep industry expertise and powerful experience technologies to help financial services organizations unify data, orchestrate journeys, and deliver customer-centric experiences that build trust and drive growth.

Get in Touch With Our Experts

Perficient is proud to be included in The Organizational Change Management Services Landscape, Q4 2025 by Forrester. We believe this recognition reflects our continued momentum in helping enterprises navigate complexity, accelerate transformation, and deliver measurable outcomes through strategic change. 

In the report, Perficient is listed as a consultancy with a geographic focus across North America, EMEA, APAC, and LATAM, with an industry focus in financial services, healthcare, and utilities.  

Forrester asked each provider included in the Landscape to select the top business scenarios for which clients select them and from there determined which are the extended business scenarios that highlight differentiation among the providers.  Perficient is shown in the report for having selected extended business scenarios including Declining Performance, Process Improvement/Engineering, and Volatility as top reasons clients work with them out of those business scenarios. Perficient believes these are areas where change is not optional, but essential. 

Built to Lead with Change 

Forrester defines organizational change management (OCM) as: 

“A method that companies use to evolve their capabilities via people, process, and technology changes. OCM’s success rests on the organization’s ability to continuously sense and respond to changing requirements in order to generate the scale of change at strategic, operational, and leadership levels.” (The Organizational Change Management Landscape, Q4 2025, Forrester) 

This aligns with our approach at Perficient. We integrate strategy, execution, and innovation to help clients build adaptive organizations that thrive amid disruption. Our change strategies are designed to scale, sustain, and deliver impact across the enterprise. 

AI Is Reshaping Change—and How It’s Managed 

AI is transforming how work gets done and how change is delivered. At Perficient, we take an AI-first approach to change. We’ve helped clients launch AI-powered research platforms, deploy virtual assistants for over 30,000 employees, and define governance frameworks to accelerate the responsible adoption of AI. Our proprietary platform, Envision, connects strategy to execution. Using intelligent tools to assess readiness, identify capability gaps, and prioritize high-impact initiatives. 

We also use AI to streamline how we deliver change. From avatar-based training to multilingual narration, our team leverages AI to move faster and reach broader audiences with precision. 

Human-Centered, Outcome-Focused 

Change fatigue is real. So is the cost of poor execution. That’s why our approach is grounded in empathy, data, and measurable outcomes.  

We help leaders prepare their people to lead in an AI-powered world by: 

• Aligning change strategy with business ambition 

• Assessing impact and readiness across the organization 

• Designing enablement programs that drive adoption 

• Engaging stakeholders with clarity and purpose 

• Communicating change with precision and relevance 

Whether you’re facing declining performance, reengineering processes, or navigating volatility, Perficient is ready to help you lead through change, with purpose and precision. 

Explore the full report to see how we believe Perficient and other providers are shaping the future of organizational change: The Organizational Change Management Services Landscape, Q4 2025

Forrester does not endorse any company, product, brand, or service included in its research publications and does not advise any person to select the products or services of any company or brand based on the ratings included in such publications. Information is based on the best available resources. Opinions reflect judgment at the time and are subject to change. For more information, read about Forrester’s objectivity here . 

The Rise of AI in Banking

AI adoption in banking has accelerated dramatically. Predictive analytics, generative AI, and autonomous agentic systems are now embedded in core banking functions such as loan underwriting, compliance including fraud detection and AML, and customer engagement. 

A recent White Paper by Perficient affiliate Virtusa Agentic Architecture in Banking – White Paper | Virtusa documented that when designed with modularity, composability, Human-in-the-Loop (HITL), and governance, agentic AI agents empower a more responsive, data-driven, and human-aligned approach in financial services.

However, the rollout of agentic and generative AI tools without proper controls poses significant risks. Without a unified strategy and governance structure, Strategically Important Financial Institutions (“SIFIs”) risk deploying AI in ways that are opaque, biased, or non-compliant. As AI becomes the engine of next-generation banking, institutions must move beyond experimentation and establish enterprise-wide controls.

Key Components of AI Data Governance

Modern AI data governance in banking encompasses several critical components:

1. Data Quality and Lineage: Banks must ensure that the data feeding AI models is accurate, complete, and traceable.

Please refer to Perficient’s recent blog on this topic here:

AI-Driven Data Lineage for Financial Services Firms: A Practical Roadmap for CDOs / Blogs / Perficient

2. Model Risk Management: AI models must be rigorously tested for fairness, accuracy, and robustness. It has been documented many times in lending decision-making software that the bias of coders can result in biased lending decisions.

3. Third-Party Risk Oversight: Governance frameworks now include vendor assessments and continuous monitoring. Large financial institutions do not have to develop AI technology solutions themselves (Buy vs Build) but they do need to monitor the risks of having key technology infrastructure owned and/or controlled by third parties.

4. Explainability and Accountability: Banks are investing in explainable AI (XAI) techniques. Not everyone is a tech expert, and models need to be easily explainable to auditors, regulators, and when required, customers.

5. Privacy and Security Controls: Encryption, access controls, and anomaly detection are essential. These are all done already in legacy systems and extending it to the AI environment, whether it is narrow AI, machine learning, or more advanced agentic and/or generative AI it is natural to ensure these proven controls are extended to the new platforms. 

Industry Collaboration and Standards

The FINOS Common Controls for AI Services initiative is a collaborative, cross-industry effort led by the FINtech Open-Source Foundation (FINOS) to develop open-source, technology-neutral baseline controls for safe, compliant, and trustworthy AI adoption in financial services. By pooling resources from major banks, cloud providers, and technology vendors, the initiative creates standardized, open-source technology-neutral controls, peer-reviewed governance frameworks, and real-time validation mechanisms to help financial institutions meet complex regulatory requirements for AI. 

Key participants of FINOS include financial institutions such as BMO, Citibank, Morgan Stanley, and RBC, and key Technology & Cloud Providers include Perficient’s technology partners including Microsoft, Google Cloud, and Amazon Web Services (AWS). The FINOS Common Controls for AI Services initiative aims to create vendor-neutral standards for secure AI adoption in financial services.

At Perficient, we have seen leading financial institutions, including some of the largest SIFIs, establishing formal governance structures to oversee AI initiatives. Broadly, these governance structures typically include:

– Executive Steering Committees at the legal entity level
– Working Groups, at the legal entity as well as the divisional, regional and product levels
– Real-Time Dashboards that allow customizable reporting for boards, executives, and auditors

This multi-tiered governance model promotes transparency, agility, and accountability across the organization.

Regulatory Landscape in 2025

Regulators worldwide are intensifying scrutiny of Artificial Intelligence in banking. The EU AI Act, the U.S. SEC’s cybersecurity disclosure rules, and the National Insititute of Standards and Technology (“NIST”) AI Risk Management Framework are shaping how financial institutions must govern AI systems.

Key regulatory expectations include:

– Risk-Based Classification
– Human Oversight
– Auditability
– Bias Mitigation

Some of these, and other regulatory regimes have been documented and summarized by Perficient at the following links:

AI Regulations for Financial Services: Federal Reserve / Blogs / Perficient

AI Regulations for Financial Services: European Union / Blogs / Perficient 

The Road Ahead

As AI becomes integral to banking operations, data governance will be the linchpin of responsible innovation. Banks must evolve from reactive compliance to proactive risk management, embedding governance into every stage of the AI lifecycle.

The journey begins with data—clean, secure, and well-managed. From there, institutions must build scalable frameworks that support ethical AI development, align with regulatory mandates, and deliver tangible business value.

Readers are urged to read the links contained in this blog and then contact Perficient, a global AI-first digital consultancy to discuss how partnering with Perficient can help run a tailored assessment and pilot design that maps directly to your audit and governance priorities and ensure all new tools are rolled out in a well-designed data governance environment.