Raising the “IQ” of Digital Transformation for Commercial Banks
Part one of this two-part series described digital transformation, how it benefits commercial banks, and how banks can get on an accelerated path. This second part explores the one factor that gives banks a competitive edge in digital transformation: an intimate and well-documented knowledge of customers.
Commercial banks’ biggest untapped asset is the wealth of historical and real-time data it inherently collects about its customers. The #1 essential ingredient of successful digital transformation, this data provides the real “brains” behind the “brawn” of digitized processes, embedded security, and mobile-first UX design.
By tapping into historical and real-time data, banks can understand their customers’ business activities and preferences more intimately. This effectively raises financial institutions’ “rational IQ” and “emotional IQ” regarding customer behavior and preferences.
Banks can engage with customers on a hyper-personalized level at scale—for example, providing micro-task automation, advice and in-situ guidance on cash flow forecasting. On the emotional IQ front, technology startups like MIT-spinoff Affectiva are pushing the edge of the envelope in measuring customers’ emotions. It’s not too-farfetched to think of seeing such technologies appear in daily commercial banking.
Armed with data and better analytics, banks can deliver new levels of operational productivity to customers in key areas such as payments and cash management as well as fraud detection and protection.
Imagine making proactive suggestions to a corporate treasurer for moving money based on an end-of-day cash positions or for the lowest-cost-option to complete a transaction. Or pre-emptively sending a payroll clerk a helpful draft of her regular, twice-monthly batch check run, for review, modification, and execution.
By analyzing behavioral data, banks can deliver embedded, intelligent security that continually gets smarter about customers’ habits and patterns. For example, analytics-powered real-time transaction blocking interdicts bad transactions in process and alerts investigators for review when necessary. Preventing payment fraud or money-laundering attempts can reduce the risk of losses and fines to corporates. Behavioral monitoring understands normal transaction patterns and user activity, and identifies aberrant activity (such as unusually high counts or amounts) based on past behavior and profiles. Persistent “record-and-replay technology” can prevent employee or executive theft by tracking, to the screen and keystroke level, access to bank systems.
AI and Machine Learning
The “how,” of course, is artificial intelligence (AI) and machine learning (ML).
AI and machine learning allow computer programs to take over much of the cognitive overhead behind making decisions based on massive amounts of data, in a timely and transparent fashion. In particular, the predictive-analytics capabilities of AI and ML models are invaluable in the daily banking and finance world. AI, ML, and analytics are becoming increasingly standard features of SaaS digital banking solutions, although some are more advanced than others.
Indeed, some critical commercial banking activities are becoming virtually impossible without AI and ML. For example, screening new commercial accounts effectively against anti-money-laundering regulations like OFAC is a competitive necessity these days—both to prevent fines to banks and prevent lost business opportunities (a number of the +$1.2 billion in civil penalties levied by OFAC in the first six months of 2019 were on corporates, not banks.) Being able to quickly and confidently perform beneficial-ownership checks might shave time and angst from the commercial customer onboarding process, legendary in the past for its complexity. Data, AI/ML and analytics would likely be instrumental in commercial banks’ plans to move into more-profitable service offerings like risk management, transaction banking or advisory services.
Think of machine learning as a tenacious watchdog that can identify new potential fraud patterns and incidents. It discovers previously unknown anomalies, enhances rules engines to reduce false positives, and learns and updates continuously by applying new analytics technologies to process monitoring.
AI and machine learning can also really bring the bank and its brand to life, powering a near-human-like digital face. It can enable commercial banking that increasingly anticipates, not just automates; advises, not just transacts; and acclimates to customers, not just assumes, then acts accordingly (including autonomously in some cases).
AI-fed personalization gets better over time, delivering to commercial bank customers the same kind of personalized digital experience they have come to expect in their daily lives as consumers. This can deepen the trust and familiarity that commercial customers have in their banks while making the banks more digitally competitive.
In its 2018 Digital Transformation predictions, IDC projected that “by 2020, investors will view digital businesses differently, with specific measures based on platform participation, data value, and customer engagement accounting for over 75% of enterprise valuations.”
AI and machine learning are becoming more broadly available to commercial banks today, including through emerging SaaS solutions—clearly not “your father’s digital banking platforms.” With Digital Transformation technology options like this becoming more readily available, it’s time for more commercial banks to get on board and reap the benefits.
To read part 1 in this 2-part series, click here to find out what is meant by “digital transformation” and why it’s important to your bank.
And for further insights into the banking industry and beyond, subscribe now and stay up-to-date on the latest tips, trends, and topics.