The use of AI and ML in the BFSI industry has allowed companies to not only identify and prevent fraud, but also get a deeper understanding of their consumers’ expectations from the institution.
Technological breakthroughs have had a significant impact on how we live and execute transactions. The banking and financial services market has always been a forerunner of technology and has profited immensely from it, but in this ever-changing environment, financial institutions find themselves susceptible as transaction volumes increase fast owing to digitalization. This is exacerbated by fraudsters growing sharper and making use of new technologies. Conventional methods of identifying fraud can no longer keep up with the speed with which scammers may deploy new techniques to funnel out money.
Financial institutions have begun to actively endorse and strengthen their toolkits for engaging with customers online, utilising techniques that allow chatbots, recommendation engines, mobile apps, contactless electronic payments, digital verification and more.
As the usage of digital banking apps continues to grow, the need to safeguard payments against fraud grows even more critical. Banking institutions are now focusing not just on financial risk reduction, but also on real-time fraud detection. Banks are seeking a comprehensive picture of the risk scenario and implementing a multi-layered defense system for a balanced approach in successful detection and deterrence. They are attempting to strike a balance between rigorous anti-fraud measures and offering a good customer experience, with the objective of not just reducing fraud-related losses but also reaching aggressive revenue targets.
Going by the trend, there has been a sharp increase in the number of bank fraud instances in recent years. In the financial year 2021, the Reserve Bank of India (RBI) reported bank frauds amounting to 1.38 trillion Indian rupees. (ref) In essence, India has witnessed a sharp increase in banking fraud over the last decade, both in total numbers and in value..
To prevent fraud, banks are increasingly relying on sophisticated alternatives that combine Artificial Intelligence (AI) and Machine Learning (ML) technology. A solution like ML is capable of dealing with enormous amounts of data from several sources and detecting aberrant patterns and linkages that humans are unable to discover. As these technologies can teach themselves with the data accessible to them, they can be far more successful when provided additional data. Rather than relying on antiquated methods, AI and machine learning-based systems can evaluate large numbers of data attributes across large data sets over extended time frames to discover odd behaviours that signal fraud with greater precision.
The use of AI and ML in the BFSI industry has allowed companies to not only identify and prevent fraud, but also get a deeper understanding of their consumers’ expectations from the institution. According to IHS Markit’s report, global spending on Artificial Intelligence is predicted to touch $ 41.1 billion in 2018 and reach $ 300 billion by 2030. The evolution of advanced tech and the proliferation of mainstream applications has continued to enhance the role of AI in the banking industry. The shifting dynamics of an app-driven world are allowing companies in the BFSI industry as a whole to use AI and ML and intimately combine them with key business strategies.
When developing AI-based fraud solutions, financial institutions should strive to learn and implement guidelines in order to model some of the sector’s more inventive businesses. They should collaborate not only with their technology organisations, but also with their business line managers, to understand how fraud is affecting their business, what their biggest security flaws are, what they need to do to improve customer satisfaction, and how they can incorporate customer fraud/risk metrics into their customer analytics to improve their omnichannel marketing campaigns.
Even with the growing uses of AI and ML, several companies have been hesitant to adopt it extensively with perception of high levels of complexity in doing so. Despite this, solutions are developing that provide gradual integration, with transitional phases meant to act as an intermediary between existing systems and next-generation technology, so that banking firms do not have to plunge right into the most complicated types of AI and ML. This methodology may create payment intelligence across the company – not only in the context of fraud – but also, by combining methods and operational processes and changing the machine learning processes. Financial institutions will need to adopt solutions that make use of emerging technologies such as AI and ML, which have shown enormous promise in not just detecting and preventing fraud, but also by considerably decreasing expenses.