Artificial intelligence and machine learning are emerging as the most defining tech-marvel in this new wave of financial services. The technology, along with the abundance of data, has given way to several innovative FinTech business models. Several promising players now use AI to solve some of the major problems for customers in the banking and financial services industry – think chatbots, PFM, robo-advisors, and so on.
Banks are never the ones to be left behind when it comes to tech adoption. With digital transformation and customer experience as the topmost priority, banks are now banking on AI to deliver the next-gen service to their customers. Some of the most powerful banking and financial institutions are looking to seek partnerships, investments, and in-house developments to take advantage of the application potential of machine learning and AI.
There are various use cases and application areas for AI in financial services. Whether you think of a conversational interface, software robots, recommendation engines, automated AML checks, behavioral analytics & profiling, real-time fraud detection, insightful trading, etc., AI has many use cases. However, all these use cases can be categorized into four major categories: Front-Office (customer-Focused), Back-Office (operation-focused), Regulatory Compliance, and Trading/Portfolio Management.
As part of our recent study, we analyzed 34 major banks across several geographies (i.e., US, EU, Singapore, Africa, Australia, India) to understand the trends and developments in their AI initiatives – which include projects, pilots, and experiments. As an outcome, we found that 27 out of these 34 banks have implemented AI in their front-office functions in the form of a chatbot, virtual assistant, and digital advisor. Some of the most prominent banks in this space are Bank of America, OCBC, ABN Amro, YES BANK, etc.
Out of these 34 banks, 25 banks are using AI for their operation-specific back-office processes with the use cases ranging from process automation, fraud detection, real-time authentication, intelligent receivables, etc. E.g., Bank of America, ANZ Bank, ICICI Bank, NatWest, Lloyds Bank, etc. Bank of America partnered with HighRadius and launched Intelligent Receivable, a new service that uses AI to improve straight-through reconciliation of incoming payments. Lloyds Banking Group has partnered with Pindrop, a US-based AI startup, and has implemented its AI/ML-based solution to detect fraudulent phone calls.
Out of these 34 banks, 8 are implementing AI-based cognitive capabilities in their trading and portfolio functions, with the use cases ranging from real-time trading insights, investment research to trade matching tools, etc. Some of the leading banks in this category are BNP Paribas, Credit Suisse, Goldman Sachs, Barclays, etc.
Out of these 34 banks, 8 are leveraging AI-based solutions in their regulatory compliance functions, with the areas of applications ranging from automated data management, reporting, AML, compliance, automated regulation interpretation & mapping, etc. Some of the leading banks in this category are OCBC Bank, Commonwealth Bank, Wells Fargo, HSBC, CITI, etc.
From the study, we could understand that the biggest focus of banks with their AI initiatives is conversational interfaces and virtual assistants, closely followed by process excellence and fraud detection. While fewer banks are looking at AI in compliance and trading functions, there is no shortage of opportunities to be explored in these spaces. As 2018 unfolds, we may expect several new acquisitions/partnerships by banks and AI startups. These partnerships will play a significant role in defining the perimeter for the evolving AI strategies of banks.
All in all, AI in banking and financial services is a volcano waiting to erupt.
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