an aggregator of a number of changes brought about a change in the
banking after the crisis of 2008-2009. The crisis revealed loopholes
in the banking system and how technology and innovation was the way
forward. Whether newer products and services or operations, innovative
technology held the potential to improve the overall scenario in
financial institutions and credit unions.
Today, artificial intelligence (AI),
machine learning (ML), big data analytics, robotic process automation
(RPA), natural language processing (NLP) etc., have all evolved over
time and today, this innovative technology is transforming the
financial world especially in the customer experience, fraud and risk arena.
Fintechs today are making use of artificial intelligence to curb
fraudulent activities, anomalous transactions, risk management etc.
However, anomalous activities may not necessarily always be fraudulent
in nature; they can also be linked to money laundering. Artificial
intelligence has helped minimize the exposure especially since the
type of frauds and risks have also evolved with time and technology.
and fraud is an inevitable element of any business or process and
Fintechs are devising strategies and services to curb these activities
with the help of artificial intelligence.
Artificial intelligence collects several critical information like IP
address, geo-locations, email domains, device information, operating
systems, browser agents, phone prefixes etc. Artificial neural network
and machine learning algorithms have helped implement these to extract
helpful analysis and reports for better performance and risk
management thereby, outperforming traditional statistical methods.
AI in Financial Crimes
As per reports, credit card fraud is the
most common type of fraud that results in huge losses each year. While
eliminating credit card frauds is difficult, it can be minimized by
selecting who you choose to on-board. Depending upon the data it is
fed, AI can generate a risk profile of a customer thereby intimating
financial institutions of possible risks associated with the customer.
Money laundering, a post customer
on-boarding element, is a threat to financial institutions. Artificial
neural networks have shown improvement in identifying underlying risks
by highlighting suspicious patterns. Big data analytics compares
current and historical data of customer behaviour, transaction
patterns, cash deposits, international transfers etc., thereby
assisting financial institutions in earmarking high risk profiles.
AI and Data
Fintechs are fighting fraud and risk
through AI and standard ‘data’ is a critical aspect in receiving
desirable results. The output of AI depends on the type and quality of
data it is fed and therefore, it is essential to have a standard and
quality set of data.
The role of artificial intelligence and
its sub sets have become a vital part of Fintechs and how they
function today. Artificial intelligence has allowed Fintechs to
navigate deep water and areas untouched, for instance MSMEs while
simultaneously minimizing risks associated with it.
Risk, fraud, money laundering, anomalous transactions are different
aspects of financial crime that grips financial institutions and
artificial intelligence is helping them navigate this deep water.