The financial services industry has gone through an upheaval over the past several years with “open banking,” where customers control their financial data, has replaced the traditional model. That change has forced the industry to accelerate the adoption of digital technology.
At the same time, customer data remains at the epicenter of the financial services industry, so the need to protect, store, and leverage it is gaining importance. Along with big data and advanced analytics, artificial intelligence is the new frontier in financial services’ quest to stay competitive while also protecting sensitive data.
AI can be used in financial services for demand and revenue forecasting, anomaly and error detection, decision support, cash collections, and a myriad of other use cases.
Financial services is also among the most regulated of all markets, so while it may have the resources to deploy the latest tech to create better products and services, as well as increase efficiencies, risk is always a concern.
Discover Financial Services has been slowly exploring AI to create efficiencies in its processes, such as summarizing customer service iterations and fraud detection.
Raghu Kulkarni
Raghu Kulkarni
Raghu Kulkarni is senior vice president and chief data science officer for Discover Financial Services, and one of the first things he did before rolling out the first large lanaguage model (LLM) at the firm was to create an AI governance council to ensure repeatable processes and safeguards.
Kulkarni spoke to Computerworld about his approach to deploying AI and what guardrails his team established to ensure its safe but productive use.
What’s your role at Discover? “There are two or three parts to my role. The first part is to develop decisioning/scorecard models. What do I mean by decisioning models? For example, when people apply for a credit card, we develop underwriting models to assess the potential risk of default. This is important specially for card loans as these are unsecured loans. We develop credit line management models. We develop models that can detect potential fraud or money laundering. So, behind the scenes there is a lot of analytics that happens across the customer life cycle. In summary, our shop develops machine learning models, which assist in underwriting, line management, detect fraud, collections – the entire [financial product] lifecycle of a customer. Through the development of these models, we also work closely with second and third lines of defense to ensure model risk, compliance and legal risk are accounted for. Beyond that we need a platform on which to develop these models and implement these models. So, the engineering of how we backend the data science also falls under my purview.”
In what ways has Discover utilized AI to create efficiencies, improve customer service, etc.? “First, you want to ensure responsible AI. So, ensuring there are no biases and it’s accurate. My first job…
2023-10-15 16:24:02
Source from www.computerworld.com rnrn