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March 4, 2021

COVID-19 has accelerated the digital shift for banks across the globe by at least three to five years. As more customers become accustomed to branch-less banking, digital banking has seen greater uptake across origination, advisory and servicing. According to one research, more than 25% customers aren’t likely to return to branch post COVID. As banks across the globe have taken the pandemic and the resultant shift in consumer behavior in their stride, few key trends have emerged around leveraging data and analytics to drive the next wave of growth in banking and bring a paradigm shift in the banking channel mix.

  • Customer journey analytics: As digital becomes default, leveraging data on various customer touchpoints across channels in order to plot cross-channel customer journey and identify friction points for optimizing CX and increasing retention is becoming vital.

  • Personalization: Better understanding of cross-channel customer data in turn is enabling AI-driven, look-alike modeling, next best action and next best conversation initiatives resulting in the delivery of highly personalized experiences to customers, and much more targeted marketing efforts.

  • Operating effectiveness: Altering the channel mix has led to a change in the staffing mix - as the back-end staff see a tremendous increase in activity and front-end staff are reskilled for back-end jobs. This is requiring AI-led initiatives to increase operating effectiveness and make AI-driven HR decisions related to hiring, job fitment, job allocation, and so on. Increasing digital push is also reducing the physical presence by way of branch closures. Truist bank plans to close 226 locations in Q1 and expects about 800 total closures by Q1 2022, Keybank plans to close 70 branches by mid 2021, amounting to 7% of its branch network .

  • Financial crimes: In a report released by Arkose Labs, fraud attack volume in the first half of 2020 doubled over the numbers in the previous six months. Digital banking is also leading to more fraud and money-laundering instances, requiring banks to augment existing rule-based fraud and/or AML systems with machine learning algorithms.

  • Data governance: Despite banks investing millions of dollars in ensuring accurate and timely regulatory reporting, there are still chinks in the armor, resulting in multi-million dollar penalties. In the wake of increased regulatory scrutiny, banks are doubling emphasis on digitized data governance and end-to-end data lineage to enable quality, auditability and traceability of data used in reporting and machine learning.

  • Machine learning infrastructure – As business units start to embrace machine learning for taking business decisions, it has become imperative to make available the right infrastructure to enable the development of AI models at enterprise scale. As most large banks are yet to adopt cloud in a big way, having a scalable and sufficient on-prem infrastructure for machine learning model development and implementation is the need of the hour.

  • Machine learning operations (ML Ops): Banks have become more conscious about establishing the right ML-Ops processes to move from batch learning to online learning and extend the governance frameworks – hitherto adopted for risk modeling – to larger machine learning. Having the right technology stack for ML development and deployment, integrating AutoML platforms in the ML ecosystem and back it up with properly governed processes have become paramount.

  • Ethical and explainable AI: – Given the high level of regulatory scrutiny, banks are increasing efforts to ensure the AI-driven actions taken by them are devoid of any biases toward specific set of customers, and that the decisions taken through AI can be easily explained and traced back to the components that led to that decision.

In the post-COVID world, fintech is posing an even greater risk to traditional banking. If Citibank’s collaboration with Google Pay is anything to go by, it portends a paradigm shift in banking as we know it in favor of AI-first organizations, who have digital in their DNA, and continuous rise of the fintech. In order to survive the fintech onslaught, banks will need to both collaborate and compete with them by converting themselves into AI factories with a digital core, and those that do this well will win tomorrow’s banking wars.

Pranav Kansara is an associate general manager with over 17 years of experience in analytics, primarily in the banking and financial services sector, across risk analytics, customer analytics and marketing analytics. He is passionate about translating advances in business analytics to business outcomes, leveraging visual analytics as well as AI/ML interventions. He has led solution development across a range of data science and machine learning projects and has extensive knowledge in the fields of NLP and deep learning. Pranav holds an MBA in Finance, and is a CFA® and FRM® charter holder.

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