Properly harnessed and analyzed, the ever-growing wealth of information can generate a feast of benefits—from more personalized messaging to more focused risk analysis to enhanced fraud protection.
But the data is often trapped in silos, it’s hard to decipher and staff charged with making sense of the data often lack the training to effectively command the rapidly evolving technologies to read the tea leaves.
But help is on the way. In the lead story of this Executive Report on data and analytics, contributing writer Dawn Wotapka describes how artificial intelligence is bringing transformative advancements to data analysis in financial services institutions.
Dawn spoke to several experts on the topic, including Christine Parker of Crossroads Advisory Partners, who said, “Data analytics have been used for several decades now, and by leveraging the power of AI, financial institutions can gain an even keener competitive edge, gain operational efficiencies and make more informed management decisions in a rapidly evolving industry and customer and talent management landscape.”
Contributing writer Katie Kuehner-Hebert interviewed several more experts for her story on the new tools of data analysis. Alex Kwiatkowski, SAS’s director of global financial services, says any bank can now tap the sort of technological capability that was once reserved for the deeper pockets of bigger banks because it wasn’t affordable. The size barrier is now a thing of the past, he says.
Regardless of an institution’s size, a shiny new suite of data analysis tools must be accessible and comprehensible to the staff charged with deploying them. The training of in-house data scientists should be continuous, Kwiatkowski says. “They need to keep finding ways to do things differently to get better results, and then they need to refresh their analytics models. When models aren’t continuously reinvigorated, their effectiveness degrades over time.”
This month’s Q&A features Sam Savage, executive director of the nonprofit Probability Management, who believes financial services organizations must develop a more sophisticated approach to probabilities. They need to change the way they deal with uncertainties and stop using averages because they are not robust enough, given banking complexities. Banks should also bring top decision-makers, data scientists, statisticians and the IT department together to shape a more accurate and valuable predictive method.
“In many places, they’re still using averages,” Savage says. “And of course, that masks risks and opportunities.”
Also in this month’s Executive Report:
For bank marketers, transaction data can lead to increased revenue. Alkami’s Joan Clark says behaviors within digital banking—including spending patterns and login behavior—all produce critical signals that can inform message delivery to bolster the bottom line. She adds: “AI and machine learning (ML) can efficiently mine invaluable insights from a variety of account holder data sources, without necessitating additional headcount.”
How data-driven insights transform digital banking. Apiture’s Ajay John says banks and credit unions have a wealth of available data about their account holders that can be used to better understand their current activities and potential needs. “Financial institutions that can tailor the digital banking experience despite minimal in-person interaction will set themselves apart,” John says. “Building a consultative relationship with consumers, one that supports their financial literacy, savings goals and debt avoidance, for example, can shift the relationship from purely reactive and transactional to one that proactively addresses consumers’ needs.”
Taking a new approach to portfolio management in a complex landscape. Chris Stanley of Moody’s Analytics says AI-enabled processes are reshaping how bank executives are identifying actionable insights for enhanced operating leverage across the risk-analysis lifecycle. He adds, “Portfolio monitoring is an important starting place for this transformation, as existing customer data is already available and represents embedded risks that shape the trajectory of future growth.”
Fighting financial fraud with open-source AI. According to Srikrishna “Kris” Sharma of Canonical, the analysis of vast amounts of data allows banks and credit unions to better identify fraud patterns and anticipate and detect emerging ones. AI techniques, including anomaly detection, machine learning models, natural language processing (NLP), neural networks and deep learning models, allow financial institutions to combat sophisticated fraud schemes that evade traditional methods. Anomaly detection involves identifying unusual patterns or behaviors in data that deviate from the norm.
We hope you will find actionable insights on data and analytics in this BAI Executive Report. As the experts here agree, smart use of data and analytics will increasingly drive the success of financial services organizations. Artificial intelligence adds a dynamic new layer to the process and will allow smaller institutions to compete with banks with more substantial IT budgets and teams. Feel free to contact us with your thoughts on banking’s evolving use of data and analytics.
Edmund Lawler is a contributing editor for BAI.
We offer actionable insights on other data and analytics topics that can benefit financial institutions in the BAI Executive Report, “The insights of data and analytics.”
