In my last blog post, I introduced data democratization. Today, I’ll share an approach to architecting a metadata search solution that enables data democratization.

There are a number of ways to craft a comprehensive guided search of a firm’s data catalog and governance metadata repositories, each with its own cost/benefit profiles. For example, creating a single data model that integrates metadata from a set of enterprise data governance tools (glossary, catalog, quality, lineage, etc.) and uses a purpose-built interface can provide a guided user experience, it is also the most time-consuming and costly approach.

A more efficient approach is to leverage the capabilities of AI-enabled search tools, in conjunction with a data abstraction/indexing/connection layer, to provide the intelligent search capabilities required for an optimal user experience. In this construct, the subject metadata does not have to be extracted from the respective governance tools, reorganized, and redundantly stored. Instead, the abstraction layer performs the logical normalization and indexing necessary to access the various metadata.

In this scenario, the AI-enabled search tool accesses the data governance metadata stores via the abstraction/connection layer, enabling a unified experience across multiple silos while seamlessly directing users back to the content-native environments to view the detailed results. Over time, the AI-enabled search gains intelligence. It collects context and signals from users to drive holistic relevance and gathers analytical insights to drive reporting, refinement, and machine learning (ML)-driven improvement.

In my next post, I will highlight different approaches to developing a metadata search solution.

In the meantime, if you are interested in learning more about this topic, consider downloading our new guide The Search for Data Democratization in Financial Services.

In my 30-plus years of leading and supporting data programs for major financial brands, data democratization has gone from wish lists to a must-have. Over the next few blog posts, I will discuss the concept of data democratization and why it’s critical for financial institutions, from banks to insurance companies, to embrace it.

Data is among a financial services firm’s greatest assets. If properly governed, it provides the foundation for decision making, regulatory compliance, competitive advantage, operational efficiency, customer satisfaction, and revenue generation. However, in order to realize all the benefits that can be derived from the wealth of stockpiled data, the business needs to be able to identify what data is available, its quality, and how to access it.

What Is Data Democratization?

Data democratization refers to making a firm’s data resources available to all employees across the enterprise. While simple in concept, it can be complicated in practice. A firm’s data resources are certain to contain personally identifiable information (PII), material non-public information (MNPI), and other sensitive information that must be made available only on a need-to-know basis.

In the broadest sense, data democratization often appears at odds with the very concept of data governance: the management and control of data.

There is, however, an approach that can satisfy the conflicting objectives of availability and control. The solution lies in the concept of metadata. Metadata, as a set of data that describes and gives data democratization information about other data, provides the abstraction layer necessary to allow the data resources to be searched and found without exposing the underlying data.

Depending on the maturity of a firm’s data program, metadata resources may be as basic as file/table names and a list of data fields. Alternatively, metadata can encompass full descriptions with business terms, annotations of key/principal/critical data elements, standardized taxonomies, data quality rules and scorecards, and the entire lineage that documents the life of each element, from first instantiation through consumption. The richer the metadata, the more power it has to foster the democratization of data within the enterprise.

In order for metadata to serve the organization, it must be fully accessible and searchable. By nature of their roles, data stewards and other data experts are well versed in the use of various governance tools. However, these tools alone do not serve the needs of more casual business users. Individuals outside of the data or technology functions likely do not understand the nuances of lineage, quality, normalizations, and transformations. They would also not know which tool to use to begin their investigation or which attributes to search. Instead, they simply want to find a source of information for the task at hand.

An intelligent, integrated search capability must be created to unleash the full benefits of data democratization and guide users on their data journey.

In my next post, I will highlight an approach to developing a metadata search solution.

In the meantime, if you are interested in learning more about this topic, consider downloading our new guide The Search for Data Democratization in Financial Services.

In my last blog post, I shared what a 360-degree view of data means when centered around data lineage principles. Today, in my final blog post of this data lineage series, I’ll discuss how Perficient’s Data Governance Maturity Model can help enhance your data programs.

Given the power of data lineage to augment and enforce an established enterprise data management program, it often helps to have an experienced partner on the team. Perficient’s Data Lineage Assessment and Strategic Roadmap evaluates how prepared an organization is for developing a data lineage program and creates a strategic and actionable implementation roadmap, from assessment through requirements definition and solution architecture.

As a precursor to the integration of data governance tools around data lineage, it may be prudent to assess the firm’s readiness for such an undertaking. The Perficient Data Governance Maturity Model can provide great insight into the overall state of a data governance program. Here too, Perficient’s experts can provide guidance and expertise in performing an independent, unbiased evaluation of the overall data program, revealing those aspects of governance requiring attention first.

Data Governance Maturity Model

If you are interested in learning more about this topic, consider downloading our Supercharging Data Governance in Financial Services With Data Lineage guide.

If you have any questions about our data lineage capabilities or would like to discuss the topic directly with me, feel free to reach out at David.Willner@perficient.com.

In my last blog post, I shared what an automated data lineage solution can look like when it’s implemented. Today, I’ll discuss what a 360-degree view of data means when it’s centered around data lineage principles.

In order to maximize the benefits of creating a cohesive, integrated set of governance tools, a firm should consider deploying an internal, user-facing query facility to expose the metadata resources under management. This so-called “democratization of data” allows users to find the data assets required for their task at hand, with full knowledge of its source, quality, constraints, and ownership, while not revealing any specific data content. The integrated business glossary enables the repository to be searched by definitions and business terms familiar to the user without knowing technical field or table names. Driven by lineage, the user can navigate the data required to determine the optimal point in the lifecycle to obtain their information, with all necessary standardizations and transformations intact.

This metadata query facility should also facilitate the entitlement process by integrating with the firm’s workflow management tool to ensure full compliance with established data access policies. If tied to a data repository (mart, warehouse, lake), the facility might also provide direct access to the information desired, with filtering and masking of sensitive information, as dictated by the access rights granted.

In my next post, I will share how Perficient can help enhance your data programs with our data lineage offerings.

In the meantime, if you are interested in learning more about this topic, consider downloading our Supercharging Data Governance in Financial Services With Data Lineage guide.

In my last blog post, I introduced data lineage. Today, I’ll share what an automated data lineage solution can look like when it’s implemented.

In order to reap the benefits obtained from integrating data lineage into the fabric of an overarching data governance program, each of the respective governance tools must be implemented, configured, and populated in its own right. Key data elements must be identified and their business terms defined, data quality rules must be configured and assigned accordingly, and the catalog must be operationalized to periodically scan all data stores, repositories, warehouses, marts, and lakes in order to compile the whereabouts of all data elements throughout the enterprise.

A comprehensive data lineage practice must be established to periodically scan program libraries and databases to create the map of data elements from creation to consumption. Any breaks in the automated handoff of data elements from one point to another – such as when files/tables are downloaded and massaged in an end-user tool such as Excel and then uploaded – have to be “stitched” to reestablish the data’s path.

Once the foundational data governance tools have been established and populated, the power of their integration can begin to be realized. Integration points will need to be established, driven by the APIs available within each of the specific governance tools in use. Application code will need to be written to leverage the continuity of control afforded by the data lineage information within the governance toolset.

In my next post, I will discuss the importance of making your data accessible to and searchable by users across the organization.

In the meantime, if you are interested in learning more about this topic, consider downloading our Supercharging Data Governance in Financial Services With Data Lineage guide.

Over the next few blog posts, I will share how you can supercharge your firm’s data governance programs by leveraging data lineage capabilities. The information I will share with you is based on many years of experience leading and supporting large data governance initiatives in financial institutions. Whether you’re in commercial, retail, or investment banking, asset management, capital markets, payments, or insurance, you can benefit from the use of data lineage concepts, approaches, and tools.

In today’s post, I will discuss the fundamentals of data lineage and its role within a data governance framework.

Much has been written about data lineage. Its function is to trace and document the journey of data elements, from their point of inception to all datasets throughout an organization. Regardless of how many hops, downloads, uploads, and transformations these data elements undergo, a capable data lineage tool will track the journey from system to system and table to table and help stitch together gaps across the points of manual intervention and processing.

Data lineage tools are the utility of choice for unraveling and bringing order to complex, interdependent system flows and are instrumental in identifying the “golden source” of critical data elements in an enterprise.

For example, as Secured Overnight Financing Rate (SOFR) replaces London Inter-Bank Offered Rate (LIBOR), financial institutions have leveraged data lineage tools to find all references to LIBOR rates. While this is a perfect use case for a data lineage tool and exactly why these tools exist, they can play an even more prominent role in an organization by enabling firms to supercharge their data governance programs.

DAMA International’s DMBoK (Data Management Book of Knowledge) defines data governance as “The exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets.”

In practice, data governance is the overarching term encompassing data ownership, privacy, control, security, and quality. The management of these attributes and metrics must be enforced throughout the organization, wherever the data is in transit, staged, stored, warehoused, or archived.

In support of data governance, specialized applications address one or more of these components. In some cases, it is with an integrated suite of applications, while in others, it is with a best-of-breed tool that addresses a single area.

Typically, a data governance program will leverage tools including:

• Business Glossary: Terms/definitions/classifications for key data elements
• Data Catalog: A centralized repository for all metadata elements across all datasets/tables in an enterprise
• Data Quality Rules: Quality rules for data elements; hardcoded, inferred from metadata, and/or AI/ML-based

When combined, these tools, if implemented broadly within the enterprise, effectively serve as a foundation for a data governance program. Key data elements can be fully annotated in business terms, the sensitivity of the information can be noted [e.g., personally identifiable information (PII), material non-public information (MNPI), etc.], and data repository scans by the catalog

can provide a comprehensive inventory of data fields/attributes/ characteristics. Whether assigned or derived via AI/ML inference, data quality rules can be applied at the source, repository (warehouse, lake, etc.), or at key usage points, such as regulatory reporting.

While effective, these tools, even if well integrated, yield a more manually intensive process than necessary and is ripe for gaps in control. It is in this context data lineage can transform the data governance process.

For example, if a key data element is marked at its source of origin as PII and is subsequently read, transformed, stored, downloaded, or uploaded by dozens of programs and systems through its journey, the catalog would only know of the resultant data field’s existence in the downstream data tables and files. At best, with the application of AI, the catalog might be able to infer, albeit without certainty, the field’s relationship to the key data element and its demarcation as PII.

The same loosely coupled association would apply to the business glossary and data quality rules. The further transformed or derived the resultant downstream field, the less likely even a well-trained AI/ML algorithm would be able to make the association.

Data lineage knows the whereabouts of all downstream locations of a given data element, whether unchanged, transformed, or the basis of a derivative field, and can provide the certainty needed to automatically apply the controls associated with the original element. The downstream instances (or spawn) of a given field can “inherit” the designations, privacy protections, quality rules, business terms, etc., from the source element.

When the various control attributes associated with all downstream instances of a data element are known, automated masking, encryption, and access rights can be enforced without potential gaps in governance, and without manually tagging each item.

In a similar context, the management of data access entitlements is a challenge for any data governance program. Knowing who can see sensitive data is as crucial as knowing where the data exists throughout the enterprise.

Typically, data access rights are granted on a file or table level, with some implementations driving the resolution down to the data element level, where it is supported by the associated storage capability (e.g., access to data elements within a flat file is an all-or-nothing affair). As those individuals or systems with access to data create downstream datasets containing copies or derivative versions of the original data, the original data owner (or assigned steward) often loses insight and control of the consequent entitlements.

As with the prior example of data sensitivity classifications, once data lineage is integrated into the entitlement process, any downstream datasets can inherit the entitlement restrictions associated with the original data elements, eliminating much of the manual maintenance of the function while ensuring sensitive information is protected from the onset and only viewable by those authorized to do so.

Beyond the improved visibility and control afforded by a lineage-driven data governance function, there are also significant financial benefits. Reducing the manual effort required to maintain the dynamic spawn and evolution of data across enterprise results in significant cost savings, and in turn, a superior ROI.

In my next post, I will discuss what an automated data lineage solution looks like.

In the meantime, if you are interested in learning more about this topic, consider downloading our Supercharging Data Governance in Financial Services With Data Lineage guide.

Previously, I analyzed how dirty, or bad data is the enemy of machine learning. The final blog of this series describes the advantages of artificial intelligence and machine learning in financial services.

Many financial services organizations have already begun to take advantage of ML technology because of its proven ability to reduce operational costs, increase revenues, improve productivity, enhance compliance, bolster security, and enrich the customer experience. However, most companies are in the early stages of exploiting the benefits of ML.

With more than 10,000 AI vendors competing for market share, there exists a range of ML applications for different use cases in financial services. Some applications, such as chatbots, are industry-agnostic and need to be trained for the specific company’s vernacular. Others are highly specific to financial processes. If a firm has the impetus, sufficient technology talent, and financial resources, there are open-source ML frameworks that can be leveraged to facilitate custom software development.

If properly implemented, AI can provide financial services firms with a superior customer experience (CX) and increase revenue, reduce expenses, boost security, and mitigate fraud.

Moving Forward

Given the complexities of ML algorithm selection, data science, and model training, validation, and testing, having a trusted guide, such as Perficient, can help avoid missteps and ensure success. With dedicated practice areas in AI, data management, and financial services, as well as partnerships with leading AI/ML vendors, Perficient is uniquely positioned to assist with your data management strategy and the selection and deployment of AI/ML tools throughout your enterprise. We have helped numerous financial services firms assess their data quality, identify incomplete or inconsistent datasets for remediation, and implement process and technology improvements to ensure proper edits, validations, and controls going forward.

To learn more about the specific differences between AI and ML, dirty data, and ways to take advantage of these technologies you can click here or fill out the form below.

Previously, I discussed machine learning and the traits that separate it from artificial intelligence. This blog analyzes how dirty, or bad data, is the enemy of machine learning.

While entirely accurate and complete data is the goal of a comprehensive data management program, many firms fall somewhat short. Siloed governance projects and the lack of an overall data strategy often result in an inconsistent data quality framework. The necessity of having complete and accurate data has never been more important. Training ML programs requires vast quantities of clean data, as many algorithms, such as neural networks and deep learning, gain accuracy incrementally from each set of data points. Additional clean data is required after training to test the ML models to assess their accuracy.

If a firm does not yet have a comprehensive data management program in place, it’s never too late to start. Given the competitive importance of leveraging ML technologies, firms will need to seek means of cleansing their existing data sets, as required. Within financial services, this would include any and all data that might be used in building and training a predictive ML model, including client, portfolio, market, reference, and master data. The good news is that there are many vendor software products available to identify – and in some cases, repair – suspect data elements or data records. The specific approach or method of analyzing data quality varies by product, so be careful when selecting a product.

It may seem somewhat incongruous, but ML is now being used to cleanse the data required to train other ML predictive applications. A new breed of ML-based data quality tools is emerging that is proving to be highly effective in identifying data omissions and inconsistencies. Machine learning clustering algorithms, such as k-means, provide a visual framework to identify patterns and pockets of data quality issues.

It should be noted, however, that as with most ML applications, the technology is not simply plug-and-play. It takes the hands of talented AI practitioners to determine the model dimensions and number of clusters to be analyzed, as well as to drill into and interpret the results.

Deploying ML-based solutions, or even using ML tools to prepare the associated training data, is not for the uninitiated or faint-of-heart. There is a multitude of different ML algorithms to be aware of, and a key factor in the successful deployment of ML is the ability to select the appropriate algorithm to address each situation. Even once a suitable algorithm is determined, there are numerous parameters that need to be considered for a successful model. Machine learning models can often get “stuck” on so-called “local minima” (based on their non-convex error surface) and produce suboptimal results, or no results at all.

To learn more about the specific differences between AI and ML, dirty data, and ways to take advantage of these technologies you can click here or fill out the form below.

The term artificial intelligence (AI) was first coined in 1956 by the computer scientist, John McCarty, when he held the first academic conference on the subject at Dartmouth College. McCarty, who is widely recognized as the father of AI, defined it as “the science and engineering of making intelligent machines.”

Over time, the meaning of AI has been refined to the “simulation of human intelligence processes by machines,” or “a broad set of methods, algorithms, and technologies that make software ‘smart’ in a way that may seem human-like to an outside observer.” Since “intelligence,” “smart,” and “human-like” are nebulous terms, AI has become the umbrella term used to refer to the entire class of technologies and algorithms, whether or not they have the ability to “learn” or have any real cognitive ability.

Many AI applications, such as GPS navigation, are based on statically programmed models. Statically programmed refers to the use of hardcoded, predefined logic rules to determine outcomes (results, outputs) based on input data. These systems do not learn, and the results produced for any given set of inputs will not change unless the coded logic rules are reprogrammed.

In comparison, machine learning (ML) applications, as a subset of AI, are not programmed to perform a given task, but rather to learn to perform the task. Machine learning techniques allow the software to improve performance over time as it ingests more data – recognizing trends from the data or by identifying the inherent categories within – to make accurate predictions.

Rather than hardcoding software routines with specific instructions, ML applications are trained to recognize significant elements and characteristics in the data to adjust their internal factors using various statistical approaches. Commonly used ML techniques include algorithms such as k-nearest neighbors, support vector machines, and neural networks. Deep learning is a subcategory of ML that utilizes extensive neural networks to closely represent a human brain to emulate human thought.

Recognize the Key Characteristics of Machine Learning.

There are a few key characteristics that define ML. Machine learning applications make predictions, whereas non-ML AI applications make determinations. A navigation application can determine a route from point A to point B, but an ML-based shopping application, for example, can only predict you might buy a certain product based on your purchasing history. Within financial services, similar ML-based algorithms can be used to suggest the next-best actions for a client or identify potentially fraudulent transactions in an account.

Perhaps the most salient characteristic of ML algorithms is that they are designed to learn how to perform a function, and as such, they must be trained to do so. Although there are different approaches to training ML applications (supervised, unsupervised, reinforced), they share the common characteristic that the data used for training must be correct. Training with incorrect or incomplete data will result in the suboptimal performance of the ML algorithms. It’s like trying to learn a foreign language from a textbook that has made-up words, incorrect grammar, and missing sentences.

Machine learning is on the cusp of changing business, lives, and even society. ML programs are being used to recognize cancers undetectable to the human eye, identify potential terrorist threats in social media, and allow companies to offer a personalized customer experience that eclipses the competition. In financial services, ML is playing an ever-increasing role in fraud detection, underwriting, credit risk, portfolio management, product selection, and marketing, as well as customer service.

To learn more about the specific differences between AI and ML, dirty data, and ways to take advantage of these technologies you can click here or fill out the form below.

The rise of digital-only banking is an international phenomenon. New digital-only banks, also known as neobanks, are being launched and fighting for market share in locations across the globe including the US, Canada, UK, Germany, Belgium, India, UAE, Israel, and Hong Kong.

To a customer, the advantage of a digital-only bank is the promise of superior financial returns. Without the cost of physical branch offices and the personnel costs to staff them, their expense base is greatly reduced. Digital-only banks can, therefore, offer customers lower fees and higher interest rates on deposits.

Digital-only banks are also leveraging the cloud to further reduce capital expenditures and gain agility over traditional banks with their on-premises data centers, networks, and legacy application infrastructure.

So why have neobanks not flourished? Currently, digital-only banks remain a niche market, as they have not yet realized their potential to create the necessary customer experience and offer the range of products, services, and support required to gain significant market share. Customers also worry about the security of their financial information given the limitations on the cybersecurity resources dictated by the financial constraints of some of the neobanks. In fact, Symantec, a leader in cybersecurity, stated that one major bank’s cybersecurity team would be bigger than the total headcount of all the neobanks combined.

Customer Experience

Since digital-only banks have no physical presence, their online customer experience needs to be exceptional in order to compete with traditional banks. Their mobile and web applications must provide for a complete range of transactions and customer support activities, whereas a traditional bank’s online presence is offered as an adjunct to their physical branches. As such, digital-only banks must not only provide for online account creation – including the capture of all forms, proof of identity, and signature verification, required by practice and regulation – they must also support the full range of banking services customers require. This is one area in which digital-only banks have thus far failed to meet their potential.

Many neobanks, for example, do not offer loan products, either by design or due to the immaturity of the platform. Their formula for achieving profitability is through the offering of chargeable premium accounts and services or cross-selling third-party products, such as insurance.

Since lending products, such as personal loans and mortgages, are the key income generator for traditional banks, the long-term business prospect for many of these startups is suspect.

Even if digital-only banks crack the code on providing the ultimate customer experience, they will be challenged to overcome the perceived shortcomings of the online presence of traditional banks. While the focus on providing an exceptional customer experience (CX) is not the exclusive purview of digital-only banks, traditional banks are lagging, forced to integrate their online presence with their legacy banking platforms, falling back on their physical branches to augment the customer service process where necessary.

Customers dissatisfied with the online experience of brick and mortar banks project their disappointment onto that offered by neobanks, sight unseen. Customers are unwilling to give up access to physical branch offices, no matter how much they disdain the concept, as it serves as a safety net should it be needed.

Although 70% of millennials stated they would rather visit the dentist than their bank branch, a 2019 study indicated that fewer than half of millennial respondents, ages 18 to 34, said they’d consider moving their accounts to a digital-only institution. One study found that, although 27% of US millennials felt that digital apps are the most important service their bank provides, another survey found that 43% of millennials had abandoned mobile banking activities because the process took too long or was too complicated.

Digital-only banks, therefore, need to actively promote the superiority of their customer experience to overcome the resistance to forego the security blanket of a physical bank branch. As many of the neobanks do not have the marketing resources to promote their CX, this leaves them subject to a range of preconceived, negative opinions.

Customer Demographics

Digital banking appeals to certain demographics, such as Millennials, while having little appeal to others, such as retirees. Consumers aged 18 to 24 are the heaviest users of mobile banking, with 82% of smartphone owners in this demographic segment using mobile banking as compared to only 29% of the 65+ age group. Millennials, as a demographic, have their own, unique financial issues for which they need guidance and assistance:

• Millennials are burdened with student loan debt that’s higher than ever
• Millennials have to save longer to buy a house
• Millennials are shelling out money for soaring rents
• Many millennials are struggling to build wealth
• More millennials are caring for aging parents – and spending more money doing so
• Many millennials rely on their parents for financial assistance
• Millennials need to save more money for retirement

Digital-only banks looking to differentiate themselves would be well advised to extend their customer experience plans to include advice, guidance, products, and services, to assist these customers in a strategy for managing their expenses and debt while investing for the future.

Coexistence

Launching digital-only banks with their own distinct branding have become a trend among established banks. JPMorgan Chase rolled out its digital-only challenger bank, Finn, in the summer of 2018, joining the likes of similar neobanks launched by HSBC, Santander, and Bank Leumi. It’s interesting that in January 2018 some of these banking ventures were noted as being among the 25 most important digital-only banks to watch in the future.

It’s equally interesting to note that by July of 2019, the Finn neobank was shuttered by JPMorgan Chase. This provides great post-mortem insight as to where these neobanks may flourish or why they may fail. In the year it was open, Finn only signed 47,000 customers. Industry analysts have numerous theories as to why Finn failed, but the common rationale is that the offering was not differentiated enough from Chase’s nationwide brick-and-mortar banking services. As there was no meaningful advantage for customers to sign-up for Finn over a traditional Chase account, it was not viable as its own entity.

The lesson is that traditional banks that want to launch an independent, digital-only offering must provide a unique incentive to attract clients. Regional brick-and-mortar banks could leverage a separate digital-only offering to expand their reach well beyond their current geography and potentially cater to a new demographic. National traditional banks will need to differentiate their spawned neobanks via favorable rates, lower fees, a superior CX, and millennial-specific products and services.

Conclusion

Industry analysts are lined up on both sides of the digital-only banking debate; some predict them to gain significant market share while others predict their demise over time. It must be understood that the success or failure of digital-only banking is not a singular question but rather a multifaceted issue dependent on each neobank’s range of products, services, customer experience, technological prowess, financial resources, and geographic and demographic target customers.

Even with a sound strategy and sufficient capital to support a comprehensive security program and marketing plan, a neobank must provide an unparalleled customer experience that is capable of supporting all required transactions.

Where Do You Stand?

No matter what type of bank, an independent assessment of your CX can reveal where it can be improved to gain a competitive edge. In order to be effective, this requires a comprehensive understanding of the financial services landscape, expertise in CX design, and deep knowledge across multiple technologies.

Our proprietary jumpstart tool, the CX IQ, can evaluate a company’s effectiveness in creating, delivering, and sustaining a compelling customer experience across 57 attributes/capabilities in seven CX dimensions: customer insight, strategy, design processes, enabling technologies, operations, measurement, and culture.

We offer a six-week jumpstart engagement as an effective way to engage the management team, create alignment, and guide decisions about where and how to improve CX. As part of your CX IQ engagement, you’ll receive a CX IQ scorecard, a summary of data and metrics to explain the CX score, and an action plan with a prioritized list of defined CX projects, and a full day CX workshop event.

Previously, I discussed how artificial intelligence (AI) can assist financial services firms. This blog highlights the benefits of AI strengthening cybersecurity and boosting profitability.

AI for Strengthening Cybersecurity

Another important horizontal use of AI technology relates to cybersecurity. Given the complexity of the disjointed set of security tools typically deployed in an enterprise, and the ever-changing landscape of potential threats, AI applications can continuously digest volumes of data intelligently while looking for patterns indicating intrusions or malware.

AI cybersecurity applications are being used by financial services firms to identify activity originating from malicious bots and to detect compromised login credentials, alerting both customers and companies to security breaches instantly. Some security applications are also capable of assessing the actual configuration of a firm’s security appliances and trigger alerts if suboptimal controls are found.

AI for Boosting Profitability

Specific to financial services, there are myriad potential areas in which a firm can gain a competitive edge with AI technology:

• Underwriting
• Credit decisions
• Risk management
• Fraud
• Anti-money laundering (AML)
• Market trends
• Program trading
• Portfolio management
• Personalized client advice (next-best-action)

With their ability to ingest, analyze, and recognize patterns in large data sets, AI applications can assist in better underwriting and credit decisions. Working off of a firm’s historical data stores and data points from credit bureaus, demographic information, and other sources, the applications can learn to identify those situations that support a firm’s credit and underwriting policies, reducing risk and minimizing losses. Similarly, by dynamically analyzing transaction patterns, AI applications that are focused on fraud prevention can signal alerts when potential fraudulent, money laundering, or other unauthorized activities, are encountered.

Not limited to preventing losses or fraudulent financial activities, AI technologies can be deployed to identify opportunities for increased revenue. There are AI vendors specializing in applications that can assist in recognizing market trends in real-time to benefit a firm’s proprietary trading strategies, as well as providing advice to their clients. Other AI applications are dedicated to supporting a firm’s clients by assessing the market and customer-specific data to recommend optimal portfolio allocations and advice as to the next-best-action to be taken. These customer-focused AI deployments can be an important construct in delivering a firm’s vision for improved customer experience or overall digital transformation program.

Starting with AI

With so many AI vendors and products, both cross-industry and industry-specific, it is difficult for many firms to know where to start. Some of these vendors and their products will rise to market leadership, while others will likely be absorbed into other providers or cease to exist. Selecting the right AI solution is, therefore, of paramount importance. With dedicated practices in artificial intelligence and financial services, Perficient is well-positioned to guide you through the process from concept through implementation.

Learn more about the benefits AI will have on customer service, cybersecurity, and increasing profitability in financial firms by downloading our perspective piece here, or submitting the form below.

Artificial intelligence (AI) has the ability to revolutionize a multitude of functions at financial services firms. Whether part of an overall digital transformation program or as a targeted deployment to improve customer service, increase profitability, or enhance information security, leveraging the latest advancements in AI can have a profound impact on a firm.

Artificial intelligence applications are capable of learning from experience, can adjust to new inputs, and can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns therein. The exploding interest in AI lies in its ability to rapidly consume and interpret large quantities of data and make decisions on the insight that might have been otherwise overlooked by older, static applications.

10,000 and Counting

At present, there are more than 10,000 vendors in the AI marketplace.  Some of which are providing foundational software or toolkits on which AI programs can be built. Other vendors are crafting applications that can be deployed by any firm and others are developing applications for specific markets. Broadly speaking, these AI software providers can be categorized into four segments:

1. Industry-specific (vertical) AI applications
2. Horizontal AI applications
3. AI-enabling technologies
4. AI and machine learning infrastructure

The layers progress from generally applicable technologies at the bottom to industry-specific applications at the top. Each layer may incorporate vendors and technologies from the preceding layers. The net result is that when implementing industry-specific (vertical) or industry-agnostic (horizontal) AI vendor applications, a firm does not have to evaluate, select, and implement the underlying components separately. There is a wide breadth of the AI vendor space and the rapid developments in technology. Financial services firms can select from a broad array of applications that best meet their needs.

AI for Transforming Customer Experiences

Horizontal applications include technologies, such as chatbots, that enable superior contact center interactions. This also includes applications that can integrate with social media platforms to enable companies to monitor those channels for firm-related dialog (i.e., social listening).

Such uses are paramount to providing a better customer experience, which is a key component of any comprehensive digital transformation program.

Chatbots recognize and can process natural language, either spoken or typed, to provide instant, self-help customer service. These AI bots can be deployed internally in a customer support function. They can act as expert guides for representatives to accelerate access to information and to recommend additional products or services. Social listening-enabled applications can provide a firm with real-time insight into issues, sentiment, and trends that can affect their business interests. It can enable a firm to proactively respond to customer issues through social media platforms. Thus providing an opportunity to increase satisfaction and build brand loyalty.

With so many AI vendors and products, both cross-industry and industry-specific, it is difficult for many firms to know where to start. Some of these vendors and their products will rise to market leadership. Others will be absorbed into other providers, or simply cease to exist. Selecting the right AI solution is, therefore, of paramount importance. With dedicated practices in artificial intelligence and financial services, Perficient is well-positioned to guide you through the process from concept through implementation.

Learn more about the benefits AI will have on customer service, cybersecurity, and increasing profitability in financial firms by downloading our perspective piece here, or submitting the form below.