AI FinTech: Making Data Science a Competitive Differentiator



Leaders in financial technology (fintech) companies have long known the benefits of using the data they have to build, manage, and grow the business. With recent technology advances—starting with big data, and now in the artificial intelligence (AI) and machine learning fields—fintech firms have a whole new set of opportunities for expanding both the money they manage and the products they offer in their portfolio.

With these new AI capabilities and data science, fintech teams can speed innovation. These teams can increase their focus on enhancing customer experiences and introducing innovative customer-facing products. While AI and machine learning capabilities have enabled increased customer loyalty in traditional financial companies, they have introduced a whole new type of AI-based fintech organization. These new AI fintech organizations are giving consumers novel ways to manage their money, from simplifying long-term financial planning to finding new ways to save.

Digital Enablement and Mobile Customers

While some customers still want to visit traditional branches for all their needs, the majority of customers want to be able to do their banking from wherever they are. From checking balances to depositing checks, this mobile customer base is pushing financial institutions to migrate all their services to be “digital first.” This even includes all the activities that were traditionally paper-based, such as opening an account.

A side effect of being digital first is a massive increase in the sheer amount of data that is generated and that can be captured around each process. How long did an online application process take per page? Which sections do most people ask for help on? Where are errors most common? What device is being used? Where is the customer? Are they on a car dealership lot looking at car loans?

By leveraging their AI investments and this ever-growing pool of data, data scientists can refine existing models and create new fintech-specific models. With these models, teams can more accurately target gaps where new products could be developed and better prioritize enhancements of existing products.

AI Started with Fraud Detection and Risk Management

Before mobile devices became so prominent, fintech firms started down the road of data science with a focus on addressing a couple of the biggest problems facing the industry: fraud detection and risk modeling. This early focus makes absolute sense: It is easy to prove a return on investment when you are recovering funds and reducing the number of bad loans that are made.

Let’s take fraud detection: In the past, it wasn’t possible to individually evaluate every person or every transaction. The process relied on randomly selecting the target of the day. This worked to a certain degree, but the entire system could not be policed.

In an insurance company I worked in, less than 5% of all transactions were actually audited when the process was manual. By introducing AI capabilities based on known patterns, teams were able to establish systems that could apply auditing across the entire corporate data set, including real-time data coming in from payment processors. Once they could automatically detect and report on more common types of fraud, data scientists could then begin to focus on training their AI models to address more unique and complex scenarios. For example, teams could start to investigate fraud that involved multiple accounts and multiple locations, looking for patterns associated with money laundering and other large-scale schemes.

AI Proved Itself with Product and Process Improvement

By enabling teams to segment data in a nearly infinite number of ways, data science allows fintech firms to start intelligently modeling customer behavior. The base data starts with transactions existing customers have with the fintech organization’s infrastructure. The next layers of data are gathered from customer interactions with the partner ecosystem. At this level, teams can gather data related to how customers interact with various online properties, and they can track metrics from marketing campaigns, including online and offline ads.

This pool of data and the sources it is retrieved from enables teams to introduce real-time and predictive analytics. AI fintech teams can use these analytics to gain insights on how to best target various types of potential and existing customers, so they can increase interactions with the organization’s digital assets and boost conversions and retention. The data also gives product managers the insight they need to better target their products and to identify gaps in their portfolios that are causing them to lose potential customers.

The Future of AI Fintech is about Mass Personalization

AI fintech is about more than just filling in gaps in the product portfolio. By harnessing AI and pulling from the available pools of data, teams can get a real time and actionable view of how customers choose and use each product type. AI fintech enables teams to establish a 360-degree view of the customer. With this visibility, teams can identify the best products to cross-sell to existing customers as they interact with any of the firm’s channels of engagement.

By doing AI-based modeling of customer behavior on top of these 360-degree customer views, fintech teams can start to make customers feel like every interaction is personalized. These capabilities will continue to open up new opportunities, enabling AI fintech firms to deliver ever-more valuable services to their customers.

These capabilities will also present opportunities for traditional banks and other large financial institutions. Historically, these organizations were seen as slow moving and difficult to interact with. With increased digitization and AI, these organizations are catching up with evolving trends in this sector and they are now leading the way in innovation. In the process, these organizations are providing a valuable example to large organizations across industries, illustrating the power of data science and AI.

Vince Power is an Enterprise Architect with a focus on digital transformation built with cloud enabled technologies. He has extensive experience working with Agile development organizations delivering their applications and services using DevOps principles including security controls, identity management, and test automation. You can find @vincepower on Twitter. Vince is a regular contributor at Fixate IO.


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