Updated: Dec 8, 2020
The advancements in technology and digital infrastructure are revolutionizing how businesses is conducted. Every now and then, we see new and innovative products and services that are intended to solve a given problem, bridge the existing gap or bring a better user experience.
Google, Amazon, Apple, Netflix, Instagram, Uber, Yelp, Square, etc are some of the examples that have modernized our consumer behavior as well as overall life style. All of these incorporate Artificial Intelligence / Machine Learning into their platforms to learn from the customer’s preferences, likes and dislikes, ordering history and related online behavior to show relevant products, advertisements and user-centric content.
So, what is A.I/M.L at the first place?
Artificial Intelligence is the effort to automate intellectual tasks normally performed by humans,
says Francois Chollet, an A.I scientist at Google and the author of Keras library in Machine Learning; whereas Machine Learning can be defined as the field of artificial intelligence that allows machines to learn inherent patterns and linkages amongst the data and accordingly suggest solutions to perform a given task.
Just like all other businesses, the financial sector is also adopting innovative technologies to build customized solutions to enhance their product-mix, besides offering personalized financial services to their customers. Let’s look into some of the areas where Artificial Intelligence is disrupting the existing business processes in banking.
Virtualized Assistants (VA) are replacing legacy online customer helpdesks in Banks and other financial institutions. Thanks to Natural Language Processing (NLP- a field of A.I that studies the speaking and writing patterns of humans) which allows the Chatbots to provide customer support to the clients on 24/7 basis. The banks mainly incur a one-time capital expenditure for deployment of the VA platform that saves them from the financial and human resource overheads involved in conventional helpdesk operations. As their usage is growing, Juniper research predicts that the banks could save an estimated USD 7.3 billion in operational costs by 2023.
As an example, HSBC is using its Amy Virtual Assistant to simulate human-like conversations with the customers. Likewise, Bank of America is using Erica, Commonwealth Bank (Australia) is using Ceba and SEB (Sweden) is using Aida Chatbots.
Fraud Detection is an increasingly important focus as the banking systems become more digitalized. The risks in the areas of information security and cyber frauds are escalating. No matter how secure the banks perceive their systems to be, it’s a reality that the banking industry is losing billions of dollars due to online frauds. There was a 246% increase in global credit card fraud loss from 2012 to 2017, says ‘The New Physics of Financial Services’ report by World Economic Forum. The Financial institutions, including banks, Payment Service Providers and Financial Switches are integrating artificial intelligence and machine learning into their systems to analyze patterns from the transactional data, detect frauds and generate alerts.
The Society for Worldwide International Financial Transactions (SWIFT) – a company which facilitates cross-border payments through their messaging platform, has developed an A.I powered solution for fraud control which generates alerts and blocks transactions which are detected to be fraudulent. The solution gets smarter overtime as it keeps on learning and updating itself with every passing transaction.
Banks are integrating risk management platforms that generate fraud insights by combining data from multiple channels to generate accurate risk profiles. These platforms use machine learning to process and analyze transactions worth trillions of dollars in real time.
Credit Decisions of a new loan applicant including those with no prior banking history is handled efficiently with AI/ML. As evident, a bank would not want to draw the burden of a non-performing loan on a risky customer; but on the other hand, a credit officer would not want to lose a business opportunity. The Artificial Intelligence and Machine Learning based systems are solving this problem by assessing the profile of the individuals from unconventional methods like online spending history, utility bill payments, mortgage payment behavior and others. This allows the banks to make instant and efficient credit decisions without losing business opportunities.
Advancements in Financial Technologies are not only changing our daily habits but our lifestyle as a whole. In Financial Services, Artificial Intelligence / Machine Learning is promising new and innovative solutions. In the future, we will experience additional value-added services offered by financial institutions, enhanced profits, and reduced risks through the use of disruptive technologies.