Digital transformation could mean different things to different businesses. Case in point, banks.
Let’s start with 2 news items that show how banks are transforming, digitally and literally:
News 1: HSBC: HSBC Moves $20 Billion From Paper to Blockchain In One Of The Biggest Financial Deployments
The bank is taking this move to digitize paper-based records of private placements with a Digital Vault that can give real-time access to securities records. Given the bank has $50bn of such assets, this is being looked upon as a bold move.
News 2: “NatWest is involved in a range of digital developments, including Mettle, its digital bank for small businesses. The firm has a new personal finance app Mimo, which uses open-banking application programming interfaces, artificial intelligence (AI) and data analytics to create a social feed that helps customers manager their money.”
Gartner estimates say 80% of traditional banks as we know it will die over the next decade.
Even if that exact estimate is off the mark, the fact is, given changing consumer habits, banks can no longer get by with their legacy systems and shoddy customer services.
Nimble Fintechs are nipping at their feet as unlikely competitors are emerging, starting with payments, from anybody with a strong and loyal customer base. Yes, we mean the likes of Amazon Pay and Google Pay.
Banking is necessary, banks are not, the quote which Bill Gates is supposed to have said, years back is now literally coming true.
But at the crux of all these changes, lie a more critical element. The rise of new kinds of data and analysis techniques from AI to machine learning that is actually driving the decisions behind the systems that can enable the so-called transformations.
- Credit decisions: Credit scoring was one of the earliest applications of Analytics as we knew it earlier. Today, with AI, more sophisticated rules can be developed which address the sparse data problems by factoring in alternate and behavioural data such as smart phone usage and payment behaviour
- Risk Management: Probably the single most important function in Banks today, as assessing real-time risk becomes a key ingredient to avoid losses, both financial and reputational. With AI, apart from the quantitative data, unstructured data systems can be assessed for risk management. A specific use case within is has been Fraud Management where triggers can be created when seemingly contradictory spending patterns are observed
- Trading has been another area touched by AI. Trading was anyway decision making in mere fractions of seconds. With AI, one can manage entire portfolios by identifying stock price movement trends from both unstructured and structured data sources
- Personalized banking and advice: From chatbots that can manage customer queries to robo advisors that can plan wealth management goals to customized plans for savings and expense management; AI is enabling banking to be reimagined from the users’ perspective and not from the way the bank has been organized by product groups like loans and cards.
Meanwhile, as these use cases evolve, neo banks and banking as a service are emerging as a new alternative where third parties such as developers, Fintechs and nonFintechs can develop financial services without starting from scratch. It enables these third parties to connect with the core banking systems through APIs. Here are a few already known examples of such integrations:
And at the heart of every small API and every such NeoBank and Banking as a Service transaction, there will be a new avalanche of data, which will require data science techniques, AI and machine learning to make sense of all the possible decisions.
BRIDGEi2i partners with and helps leading financial services companies control fraud detection and increase regulatory compliance besides offering other AI-powered assistance.
Author: Debleena Majumdar