In today’s era where AI and ML have become streamline services and products for companies, every industry is embarking on creating new use cases with them. Across wide range of industries, some of which are historically existing from start of mankind, every traditional way of working is being re-looked at to improve, to automate and to refresh the way it’s done.
In all this, even Banking industry which is close to $5 trillion in size globally as per 2023 figures, is not behind and is equipping itself with AI and ML intelligence to future-proof itself. All the peripheral industries that are linked to banking are using AI and ML in multiple ways. It is matter of time that banks will have to bring in AI to change as per the needs of globalization, international banking and trading and finance management areas among others. Here is my take on where next we can head to using AI and ML in Banking and Fintech as an industry that’s growing at 6-7% CAGR every year.
Automated Personal Finance Management.
We all have had our shares of influencers’ videos guiding us on personal finance management journey and how we can improve our XIRR with every penny. If influencers can be trusted why not the banks itself? Banks can embed Personal Finance Management into their portals to enable users to set goals for themselves. Based on those goals, banks can accordingly lock their debits and funds from being spent if it is taking user away from their financial goal.
Automated Audit reports
Banks need to generate their audit reports at regular intervals to comply with the regulatory bodies or for audit purposes. For the same they need to get data from multiple systems and input or generate reports with external trigger to comply with regulatory requirements. With AI and ML, banks can accelerate this process to get reports at regular intervals that can track the banks spend, their reserves, etc. to use it for checking compliance and generate necessary report.
AI to reduce cybercrimes in finance
We all have come across several schemes that people fall prey to and lose their money. AI can be a blessing for us if we can improve our predictive analytics using the past data and add more intelligence to curb the crimes in future. Banks can automate responses to cyber attacks without waiting for human intervention to fight it. After trend analysis on the patterns of attacks, AI can help in generating solutions as well. AI can therefore be made more robust first in threat detection, incident response and then using ML pro-actively predicting any possible future threats.
Self-healing banking portals
There are times where digital banking portals have crashed owing to some technical issue, data recovery and network problem among others. Some manual intervention is needed to get them up and running. Just like we have self-healing machines which can proactively repair themselves, our digital banking portals as well using AI and ML can detect such malfunction preemptively and heal itself on its own. This can eliminate the time systems have to wait for someone to repair it. Similarly, these systems are also down for maintenance activities at regular intervals, which causes inconvenience. To facilitate continuous service, AI/ML can eliminate the downtimes by breaking down the maintenance activities smartly and carrying them out seamlessly in the background.
Intelligent loan recovery
The number of NPAs and bankruptcies that are filed everyday are growing everyday adding pressure on banks’ profits and on the end customer. To facilitate this loan recovery AI and ML can assist banks. By tracking the spend patterns associated with the customer, banks can very well conclude if the about-to-be defaulter is avoiding the payments to get away or is genuinely facing the losses and hence not able to pay.
Smart Data Backups
For all financial systems, may it be bank data, customer data, credit report data and other critical information, regulatory bodies advise data backups to be taken at regular intervals. When such backup is planned other data read/write is paused until the activity is complete. This pause causes discontinuity and can be avoided if smart data backups are brought in picture. Using AI, databases can detect a change in record and take a backup to avoid the downtime. Using ML, database can learn to recover itself beforehand so that data recovery does not cause discontinuity.
Smart Budget Categorization
Based on the trends of spendings, for salaried customers, banks can smartly use AI to categorize the salary every month and create a spend analysis to make them understand how they can effectively use their funds. Going deeper into this, market conditions can also be incorporated to drive more action on how profits can be maximized with investment in real estate, stock markets, mutual funds, insurance etc.
Realtime Cross border settlements
The current process followed to settle cross-border payments is time-consuming, lengthy and involves risks. Using AI, banks can cut down on time and risk of conversion rate while ensuring competitive rates and transparent pricing. AI combined with blockchain technology can help in enhancing transparency and security in cross border payments by using shared ledger systems. Besides, AI algorithms can analyze vast amounts of data to determine the most efficient routes for transactions, reducing processing times and costs.
Borderless Banking
We all have experienced the ease of banking with UPI coming in. Now with UPI being integrated in foreign countries, borderless banking has started becoming a reality. With use of AI and ML, this borderless banking can further be enhanced to improve security, access and ease. Currency conversion, banking regulations, investments in foreign stock markets can all be made easier by bringing AI in picture. Getting better conversion rate, background check before investing in foreign stock exchanges, understanding banking better can all be solved using AI
Reducing Financing Risks for banks
In India, there have been rise in NPAs lately. There have been scams wherein underlying collateral was faked and loans were approved to fulfil the targets. This kind of practices create risky liabilities on bank which might result into fall of banks. Big banks like Credit Suisse, Silicon Valley Bank, Signature bank and many more no longer exist owing to their risky assets and liabilities. To curb such falls, AI and ML can be used to regularly monitor the health of the assets and collaterals with banks and such risky loans as well to manage credit appetite of banks.
Humans are fearful that AI would take up their jobs and eliminate the need for human intervention. I say that why not use AI for bringing in the ethics, fairness and ease that’s needed which otherwise is lagging because of human greed, illegal interventions and laziness. A secure and closed industry as banking should embrace AI to make the current system more responsible, more trustworthy and more ethical than ever before.
What’s in store for the financial industry with this AI boom would be an interesting space to watch and learn. A secure, closed, and regulatory driven domain like banking is embracing AI and ML with a spark of creativity and convenience. This will make way for new ways to interact with money and grow financial wealth securely.
About Maveric Systems
Established in 2000, Maveric Systems is a niche, domain-led, BankTech specialist, transforming digital ecosystems across retail, corporate, wealth management, cards & payments and lending domains. Our 2600+ specialists use proven solutions and frameworks to address formidable CXO challenges across Customer Experience, Assurance, Regulatory Compliance, Process Excellence and New age AMS.
Our competencies across Data, Digital, Cloud, DevOps, AI and automation helps global and regional banking leaders as well as Fintechs solve next-gen business challenges through emerging technology. Our global presence spans across 3 continents with regional delivery capabilities in Amsterdam, Bengaluru, Chennai, Dallas, Dubai, Kingdom of Saudi Arabia, London, New Jersey, Pune, Riyadh, Singapore, Sweden, Dubai and Warsaw.
Our inherent banking domain expertise, a customer-intimacy-led delivery model, and differentiated talent with layered competency – deep domain and tech leadership, supported by a culture of ownership, energy, and commitment to customer success, make us the technology partner of choice for our customers.
