8CIOReview | | MAY 2022IN MY OPINIONBy Uday Shetgeri, Executive Vice President Strategy and Architecture, Frost BankA rtificial intelligence and machine learning (AI/ML) is a game changing technology, primarily driven by the tech giants like Google, Amazon, Apple, Facebook and Microsoft. It is rooted in statistical methods to predict the probability of an event happening. Instead of formulating algorithms based on a hypothesis and coding the algorithm to process data, as in traditional programming, machine learning uses historical data to develop the hypothesis and the create a program (the "model") to predict outcomes when processing new data.Proofs of concepts are emerging from the shadows of laboratories and being operational in production deployments.There are several drivers for this change, the main ones being· Low interest rate environments and the resulting margin pressures causing banks to look at ways to look at ways to increase revenue · Competitive pressure from fintechs encroaching into the more profitable aspects of banks' value chain (credit origination)· Changing attitudes of consumers towards tech giants as providers of financial services· Softening stance of regulators on use of these technologies that benefit consumers.· Pandemic causing transient hits to creditworthinessIn loan underwriting, AI/ML models better predict a good credit risk based on features beyond the traditional FICO scores and debt to income (DTI) ratios and help approve more applications without increasing risk. The models help navigate past the pandemic triggered events on the credit profile.Customer attrition is another area banks can benefit from using the technology, with the models providing strong signals of an impending departure and potential actions that can be taken to prevent it. In fraud detection, models help distinguish between genuine customer transactions that appear as anomalies and indications of a compromise on the account, and recommend and take the right action, thereby reducing "false positives" and improving customer experience.More recently,AI/ML models have enabled banks to tailor products and services to suit individual needs, creating a "segment of one" not just at the point of sale but through the customer journey from sale through life events, providing a unique individualized experience through "mass customization."Self-service channels, mobile apps, chatbots and websites can leverage AI technologies like Natural Language Processing and sentiment analysis to assess a customer's mood and trigger a transfer to an agent to respond with the required level of empathy.Data is the critical resource that the machines use to build the AI/ML models. From a data engineering perspective, three aspects of data need to be addressed: Quantity, because the larger the data set, the better it is for model training; Quality, ensuring completeness with missing data replaced with appropriate substitutes; and data Devoid of bias, being OPENING NEW DOORS THROUGH ROBUST TECHNOLOGIESUday Shetgeri
< Page 7 | Page 9 >