Automating Core Banking Operations Through AI-ML: Why and How

We are sailing through the year 2021 and technology is the default way of operations; especially for key industries such as banking and financial services. AI-ML has been spearheading the technology revolution. It is enabling banks to achieve high operational efficiencies and deliver extraordinary outcomes. According to a recent report, the market size of global AI in banking stood at USD 8.30 billion in 2019. It is expected to reach USD 130 billion by 2027; registering a CAGR of 42.9% during the period. Clearly, these cutting-edge technologies are on their way to create many more breakthroughs in the world of banking in the years to come.

Take a sneak peek into the evolving expectations of the masses in conducting their financial transactions. The banking consumers of today prefer a smarter and more streamlined approach to deal with their money matters. They look for a quick, personalized experience that is extremely safe, accurate, and devoid of any complexities. As a response to this, more and more banks across the globe are making a steady switch from their legacy systems to AI and ML-based software, which automates the myriad complex processes and leverages data intelligently to enhance their operational capabilities multi-fold. Consequently, banks can elevate their customer experience and also yield a positive, higher ROI.

Traditionally, leveraging the voluminous data generated on a day-to-day basis was a key challenge for banks. However, armed by AI and ML technologies, the new-age banks of today can delve deep into this data and extract usable insights in real-time. This allows them to effectively analyze transactions, understand and predict customer behavior, detect fraudulent activities and mitigate risks. This enhanced ability to analyze complex data sets to make informed decisions for their banking products, services, and operations is likely to fuel the demand for these technologies going forward.

Moreover, there has been a pressing need to control and prevent instances of banking-related frauds, which is also expected to drive the market for these next-gen solutions enormously. As the risk monitoring and management processes get automated with these technologies, the process of anomalies or fraud detection becomes fairly simple. Right from conducting the initial KYC checks to approving loans or taking important credit decisions, the algorithms efficiently assess the likelihood of default by a customer and highlight their risk profile. Using these insights, banks can formulate better financial/ credit risk management strategies to ensure utmost safety and security.

Further, an evolving competitive banking landscape will make it necessary for banks to grow and build on their existing technology base to fulfill the modern and evolving demands of the banking customers. According to McKinsey Global Institute, using AI to improve core banking operations and tailor services will deliver the value of over $250 billion across the industry.

As a positive step, many banks have already transformed their front-end as well as back-end processes by adopting the emerging AI and ML techniques. Chatbots powered by AI and ML have already raised the bar of customer engagement and responsiveness. Connecting with customers via chats, personalized notifications, richer audio-visual mobile delivery channels, etc has emerged as new banking trends, which cannot be undone.

However, the road ahead is yet more bright and full of opportunities. As financial institutions look for more ways to scale up and infuse greater agility and flexibility into their systems, the potential of AI and ML is further going to be tapped over the coming years. The future of banking in the modern landscape is certain to be defined by innovation.

The author of the article is Neel Juriasinghani, CEO and Co-founder, DataCultr.