BY LAUREN SARTWELL, STEPHANIE WHITE BOOKER, AND POORANI JEYASEKAR
When most bankers think of Artificial Intelligence (AI) and Machine Learning (ML), they likely think of underwriting models and chatbots. However, as these advanced technologies become more widespread, the potential use cases are virtually limitless.
Unfortunately, there seems to be a general lack of understanding about how these new technologies are being used in financial services. For example, business and marketing teams may not recognize the AI/ML driving many of their tools, and likely do not fully understand the risks they may pose to the organization. Compliance may not be consistently included in key decisions and processes that could have a big impact on the organization’s risk profile.
Financial institutions of all shapes and sizes need to understand exactly where AI and ML are being deployed - throughout the entire product life cycle of their products and services - and ensure they’re taking steps to mitigate the regulatory and reputational risks they raise in accordance with the institution's risk tolerance. With regulators squarely focused on fair lending and UDAAP, there is extra scrutiny on any potential discriminatory output of these models. Below are a few examples of where AI/ML are hiding in plain sight and why it’s incumbent on the leadership of financial institutions to educate themselves before they become a risk/compliance issue.
Where is AI/ML lurking?
AI is a catch-all term that generally refers to technologies capable of analyzing data and identifying patterns to make a decision and effect an outcome, while ML allows systems to learn and improve as new information is made available without specific programming instructions.
In financial services, artificial intelligence uses advanced prediction technology based on machine learning techniques and it’s leveraged in a variety of use cases. The most obvious are the chatbots and virtual assistants offered by fintechs, credit unions, and banks, but AI/ML is also used in designing the customer experience, marketing, and customer service to name a few:
Marketing: AI/ML is being used for targeted marketing so customers receive product discounts and recommendations based on their personalized data and search history. Marketing teams or agencies may be using tools like look-alike models to find common attributes among their customer base and other users of similar products and services. They also may use propensity models to determine what characteristics make a consumer more likely to convert on an offer.
Customer service: Today’s customers demand service that is highly personalized, easy to navigate, and effective in terms of problem solving, which is why AI is the new customer service agent. Through chatbots, call routing, workflow, and pattern recognition, financial services companies are leveraging AI to automate tasks, make predictions, and get customers quickly to the best source of help.
Customer Experience: Customers are expecting the institutions they do business with to know more about them to make their experiences faster, easier, and more customized. AI/ML allows faster processing of large amounts of structured and unstructured data from multiple sources, and identifies new and expanded options for personalization in customer interactions. This enables financial services companies to engage with customers in the format they prefer and increases customer connectivity, enhancing the customer’s overall experience.
Transaction Monitoring: Systems used for customer due diligence utilize AI/ML to learn customers’ normal transaction patterns, and identify transactions falling outside of normal patterns.
Loan Processing: Reducing time to loan closing improves the customer experience, potentially avoids human error, and makes loan processing teams more efficient. Using AI/ML, loan processing systems can route applications to the most appropriate processor and automate routine processes like ordering appraisals and flood certifications.
As a starting point, financial services companies should take stock of all of their uses of AI/ML, including vendor products, before beginning to properly assess the associated risks. Stay tuned for part 2 of this Klaros blog series that explains important steps to mitigate regulatory and reputational risks that may arise from the use of AI/ML.