How AI & ML Based Core Regulatory Engine Can Help Banks
To stay ahead of techno curve, banking and financial institutions are ready to take a comprehensive approach so as to reap benefits of AI and ML in their process functioning.
AI in the finance and banking industry will actually transform the way organizations handle their revenue, connect with their customers, and measure their investments.
Also, the central deployment of AI and ML across the industries is expected to drive international revenues of $12.5 billion to $47 billion by the year 2020.
In this extremely competitive financial era, artificial intelligence is evolving rapidly. More and more institutions are employing AI and ML-based engines to stay abreast with the evolving demands of the financial industry.
However, the availability of AI-based systems relies mostly on the existing data and infrastructure, and also on the primary requirements of financial regulation.
According to recent research, the upsurge of data science in the Fintech industry depends on certain factors which include the growth of technology, regulatory acquiescence, competition in the market, and capability for improved productivity.
Machine learning is another essential advent in the tech sector which has helped companies to reduce costs by increasing output and making decisions based on inscrutable to a human agent. The intelligent algorithms used in ML have the potential to detect abnormalities and fake information in a few seconds. Thus, it has largely influenced the banking sector to deploy AI and ML into their core functionalities so as to reduce frauds and scams significantly.
How AI and ML are helping banking industries to eliminate false positives?
At the core of AI, there are ML algorithms and self-enhancing software which offers more efficiency as they are fed with more and more info and data. This will immensely benefit the financial industry and will create a huge impact on its business processing.
Due to the upsurge of regulatory requirements and screening volumes rising up substantially, the only option left to detect suspicious transactions precisely is to deploy ML in the core functioning system.
From the past few years, incidents of global money laundering have increased significantly which has led various enforcement agencies to update their AML regulatory framework so as to control illicit ways of generating income.
Presently, many financial institutions are struggling to incorporate sophisticated screening systems and are updating their existing engines to survive with the evolving rush.
Nowadays, almost all banking institutions are deploying improved identification systems. Amore rigid approach is followed in accepting new customers and accounts. Inspection of public records of customers is acquiring huge thrust, and banks and financial institutes are not hesitant to annex negative news as a reason. All this is done to eliminate the phantom of false positives in the process of screening.
The spook of false positives and negatives:
Banking and financial institutions are keen to regulate illegal transactions which may lead to huge losses and reputation destruction. From the past several years, legacy detection systems in the zone of AML detection have led to many “false positives” that must be cleared before an action is taken on the suspected moves.
This process consumes a lot of time and is quite difficult for the investigators to cope up with the process. Even if there is a 2% false positive rate out of 1M transactions, it will lead to twenty thousand false positive detections. This 2% rate is also a dream for compliance specialists as this rate could even rise up to 70% or more.
This all stands in the way of customers expecting quick and smooth banking services. Thus, many banking institutions are in a dire need to update and modernize their Compliance and KYC practices with less cost and time in the detection process.
However, the positive news is that these AML detections can be improved with the help of advanced analytics, prognostic models, and ML capabilities which will definitely take the detection process to the next level.
On the other hand, a false negative is an event where a transaction is associated with anauthorized entity but is not being perceived. This False Negative rate reveals the efficiency of the system.
The best scenario is to reduce the False Positives rate and avoid False Negatives. This can be only be accomplished through the incorporation of numerous techniques embedded in AML solutions.
Now there’s a common conviction that the implementation of analytics-led methodology and machine learning will help beat the false positive issue encountered in AML systems. ML-based risk valuation counting, alert generation, and analytical modeling are now being seriously taken into consideration to overcome false positive detections.
Use cases that illustrate the efficiency of AI and ML in banking era:
According to an official report published in the US, the most influential and largest banks in the US are largely investing in inculcating their processes with AI and ML. Some of these use cases include banks such as JPMorgan Chase, Bank of America, U.S Bank, Wells Fargo, and Citi Bank.
Let’s get an insight into some of them:
Bank of America:
Bank of America is one of the first financial institutions to offer mobile banking to its customers in the year 2007. Recently, they introduced an AI-based virtual assistant dubbed as Erica- considered as the most projecting financial service advancement. Erica serves as a non-profit financial advisor to more than 45 million customers of the Bank.
Bank of America has incorporated AI assistant intending to improve the overall experience of their customers. They have now streamlined the process of handling routine transactions and have cleared their customer support centers as they can now deal with more complex and intricate processes quickly and efficiently.
The other notable move made in the field of AI is that Citi Bank heavily invested in FeedzAI. This is a global initiative that focuses on using data science to determine and defeat fake attempts in various avenues of financial events, which includes online and mobile banking.
FeedzAIintegratesML algorithms to evaluate huge volumes of Big Data in real-time and notify the financial institutions and banks of suspected scam cases at once.
Other than these, there are many other use cases of ML and AI which depicts that banking sector is seriously considering the impact of artificial intelligence in the regulatory framework, which in turn has led to improved automation solutions in the industry.
While banks, financial institutions, and national agencies have realized the potential of ML and its profits, they are considering ML as a human escalation tool instead of a complete automated system. They are utilizing machine learning without much fuss so as to sense doubtful activities and flag it for professionals to take action and monitor. Thus, in the near future, we’ll be able to see ML acquiring a more vital role in AML compliance and regulation.