Advancement in technology is having a remarkable bearing on the way we live and conduct business. The banking and financial services industry has always been an early adopter of technology and has greatly benefitted by it. But in a constantly changing environment, financial institutions find themselves vulnerable as transaction volumes grow rapidly through digitization. This is compounded by fraudsters being very quick to adapt to new technology to their advantage as well. Traditional means of fraud prevention can no longer keep up with fraudsters, given the pace at which they can deploy various mechanisms to siphon off funds.
According to a Juniper Research report, cybercrime currently costs the global economy approximately USD $600 billion (i.e. 0.8% of global GDP) annually, of which online transaction fraud accounts for the biggest chunk –expected to hit $25.6 billion by 2020. It means that $4 in every $1,000 spent online will be fraudulent. To put this into perspective, this amount is higher than the profits posted by some of the biggest global blue-chip companies.
It is safe to say that as the use of digital banking applications becomes more widespread, it becomes even more crucial to protect transactions from fraud. And as of now, financial institutions are focused not only on financial risk mitigation but also on real-time fraud detection. In recent times, the complexity and cost of fraud has multiplied, resulting in regulators and financial institutions to resolve this concern in real-time.
Effective detection and deterrence require that fraud strategists gain a holistic view of the threat landscape and adopt a multi-layered defense system for a balanced strategy. They are striving for a balance between robust measures to counter fraud and providing a positive customer experience, where the goal is not only curbing losses resulting from fraud but also maximizing revenue A unique approach which facilitates real-time fraud detection, thereby driving fast decision-making and responses to emerging fraud threats is the need of the hour.
It comes as no surprise then to see banks and financial institutions turning towards advanced solutions that incorporate Artificial Intelligence (AI) and Machine learning (ML) technologies. It is easy to understand why ML has caused such a stir in the fraud detection domain; it is equipped to deal with large volumes of data from numerous sources and capable of spotting abnormal patterns and links that humans are not able to identify. It is therefore natural that financial institutions are deploying it as a viable tool for fraud detection.
Fraud detection methods in India today are evolving from rules-based towards pattern recognition, with ML’s ability to recognize patterns in consumer behavior. It can also be used to protect companies from insider fraud, as it can study data access from within the organization and identify any anomalies in individuals deviating from their day-to-day jobs or exposing data to outsiders. Adding AI to the mix gives ML the much-needed edge to move beyond just algorithm-based fraud detection. Machines can be programmed to self-learn in an unsupervised model with AI so that transactions that do not conform to a set pattern are identified and therefore can be actioned upon – in real-time. Especially in the enterprise context, it’s vital that a robust fraud detection tool can collect data and detect anomalies across various channels and plugin in all the gaps to prevent further misuse.
The application of AI and ML in the BFSI space has not only enabled organizations to detect and mitigate fraud but also gain a better insight into their customers’ preferences and expectations. According to a National Business Research Institute survey, over 32% of financial institutions globally use AI for predictive analysis. With the dawn of mobile technology, the volume of data now available and the explosion of open-source software, the playing field for AI in the banking sector has widened exponentially. The changing dynamics of an app-driven world are enabling the BFSI sector at large to leverage AI and ML and align them closely with business imperatives.
Despite the emerging applications of AI and ML, there are some organizations that have held back because of the seeming complexity. However, there are solutions emerging that offer stepwise integration, with intermediary steps designed to bridge the gap between traditional systems and next-generation technology – financial institutions don’t have to jump straight into the most complex forms of AI and ML. This approach can drive payment intelligence across the enterprise – not just specific to fraud – integrating methodologies and operational processes and in doing so democratizing the machine learning process.
That said, the widespread implementation of technologies like AI and ML in India is not going to be devoid of challenges. From the dearth of credible and quality data to the country’s diverse language and socio-economic demographics, there are a multitude of challenges to overcome. But not all challenges are external. Even while being convinced of the tangible benefits of these solutions, companies are also concerned about the time, effort and expense associated with implementation. Financial institutions will need to embrace solutions that leverage emerging technologies like AI and ML that have shown great promise in not only predicting and mitigating frauds but also reducing operating costs significantly. However, an active push from decision makers is also a critical part of the equation, beyond the need for new solutions that are responsive to the ever-changing fraud threat.