The financial services industry is undergoing significant transformation, driven by the increasing adoption of artificial intelligence (AI) and data science. As financial institutions strive to stay competitive, they’re leveraging these technologies to improve customer experience, operational efficiency, and risk management. At Data Science Salon NYC, I had the opportunity to join industry experts in discussing the latest trends and innovations shaping our field. Here are four key takeaways from the event:
AI Adoption Starts with Customer-Centric Use Cases
Financial institutions are using AI to enhance customer experience through personalized services, and we’re seeing the most immediate impact in areas like call centers and knowledge retrieval. When we talk about saving time and effort, customer experience is an easy space where we can start thinking about answering questions faster.
By putting the customer at the center and leveraging AI-driven analytics, financial institutions can gain deeper insights into customer behavior and preferences, enabling them to tailor services to meet specific needs. The key is starting with use cases that have clear, measurable impact on customer satisfaction and operational efficiency.
Data Science Is About Business Outcomes, Not Just Technology
One of the most important lessons we continue to emphasize: Data science is not just about algorithms and technology; it’s about business value. In our work with financial services and insurance clients, we’re constantly focused on driving tangible business results.
When measuring success, we need to have open conversations because business leaders have very different definitions of success than technology leaders. Yes, latency is important, but at what point does that latency drive or impact revenue or costs? Ultimately, we need to put a dollar sign in front of it. Success boils down to two key metrics:
Does it move the bottom line?
Are people actually using it?
Success is defined as whether everyone can use that tool and whether it’s simple to follow. In the end, it’s people who are driving the revenue. Financial institutions that invest in data science innovation with this business-first mindset are better positioned to stay ahead of the competition and drive real growth.
AI Governance Isn’t a Yes or No Decision
One of the biggest things we’re encouraging any enterprise to do as they think about AI governance is understanding that very few evaluations come down to a “yes” or “no” decision. Rather, we should strive to define the risk mitigations necessary to get a “yes.” Effective AI governance involves establishing clear frameworks that include:
- Continuous monitoring and auditing of AI systems for bias and performance
- Transparent AI explainability to build trust among stakeholders and regulators
- Open dialogue about risk mitigation strategies
We must make sure we’re building trust beyond the vendor level, but on each individual use case. By implementing thoughtful governance, financial institutions can manage risks while still innovating confidently.
Adoption and Change Management Are Critical Success Factors
The adoption question is crucial: Are people actually using it? We need to educate our teams on what we’re doing, why we’re doing these things, and how they can take advantage of it.
One practice we always recommend is A/B testing. Many organizations don’t always A/B test the efficacy of the AI tool versus not having the AI tool. Instead of giving it to everyone at once, we’ve taken one area, split teams in half, and had one side do the work the traditional way while the other uses the new AI tool. This allows us to measure real impact and build confidence in the technology.
AI-powered solutions are increasingly being used to detect and prevent financial crimes such as money laundering and fraud through predictive modeling and anomaly detection techniques. By leveraging these technologies thoughtfully (with proper governance, testing, and adoption strategies) financial institutions can reduce risk while improving regulatory compliance.
Looking Ahead
The key to success in AI and data science isn’t just adopting the latest technology, it’s ensuring that technology drives measurable business value, is governed responsibly, and is adopted by the people who need to use it. When we get those three elements right, that’s when we see transformational results in financial services.
To learn more about Perficient’s AI capabilities in the financial services industry, visit https://www.perficient.com/industries/financial-services.
