The banking industry has access to seemingly unending amounts of data sets, and with computing power getting more refined, customer experience is unlike ever before. Artificial Intelligence (AI), Machine Learning (ML) and Predictive Analytics (PA) have had a profound impact on the banking industry, with its increasing adoption impacting both customer experience as well as routine back-end processes.
Whether it be deploying Natural Language Processing (NLP) for sorting and automating the record updation in the Customer Relations Management (CRM) solutions, or better serving customers through efficient self-service customer portals, banks are embracing machine learning in a big way.
Here are some of the key use cases:
Consumer Experience: AI-powered personal assistants, that leverage predictive analytics, and cognitive technologies, are leveraged by banks to provide personalized support to their users.
Clear Backend Processes: The banking industry is replete with repetitive, complex number of processes. Banks are harnessing AI to streamline and refine back-end processes, contributing to cost savings, better customer experience, and enabling banks to ensure that their team is able to focus on higher-value activities.
Cybersecurity: AI is being leveraged to review a huge number of transactions and flag any anomalies. AI-powered security systems are able to learn, and significantly streamline the security delivered at scale to the bank’s customers. Such AI-enabled security systems will impart a higher sense of security, while reducing any breaches, and associated risks of fraudulence.
The five of the most largest US banks are preparing for the future, by adopting Artificial Intelligence (AI) and ML in a mobile-first world where they have been able to satisfy customers operating on mobile banking with machine learning.
JPMorgan Chase has a technology budget of approximately US$10.8Bn for 2018, of which almost US $5Bn is kept for new investments. All of these AI investments are translating into making consumer experience impeccable, while cutting costs.
In May 2018, JPMorgan Chase hired Manuela Veloso from Carnegie Mellon University for the newly created position of the company's head of AI research, to complement their ongoing AI initiatives. On top of Veloso’s AI agenda would be expanding the AI footprint of JPMorgan Chase to transform all financial services.
JPMorgan Chase has developed a proprietary ML algorithm called Contract Intelligence (COiN), to analyze the credit agreement documentation and extract the important information from it. Using COiN, JPMorgan Chase was able to process 12,000 credit agreements in several seconds, instead of 360,000 man-hours.
By leveraging Amazon Alexa, the voice assistant Alexa, JPMorgan Chase made it simpler for their investment banking clients to get access to research. The assistant is serving almost 120,000 customers in the ongoing pilot phase, and when fully rolled out, it would serve all 1,700,000 of the bank customers.
In 2017, JPMorgan developed and deployed an AI system, entitled, LOXM to execute trades across its global equities algorithms business. Rather than relying on hand-coded rules developed by humans, LOXM learned from billions of past transactions how to do swift and efficient trading, including buying and selling, and critically at the best price. European trials of the LOXM have been very promising, and JPMorgan is on track to roll out LOXM across Asia, and US.
Wells Fargo created a team to develop AI-led technology and appointed a lead for its newly combined payments businesses, as part of an ongoing push to strengthen its AI offerings. Well Fargo’s AI focus is in line with the focus of the banking sector on investing in, and leveraging artificial intelligence to enable computers to perform routine tasks. Some of the ongoing Wells Fargo Projects includes systems that can spot payments fraud or misconduct by employees, to technology that can make more personal recommendations on financial products to clients.
Developing chatbots and other artificial intelligence systems has become one of Wells Fargo’s top priorities. Wells Fargo’s Facebook chatbot currently responds to basic questions about deposit and credit card accounts, transactions, and branch or ATM locations, freeing up bankers to handle more complex tasks.
The Wells Fargo mobile banking deposit customers nationwide now have access to a predictive banking features that analyzes account information, providing mobile app users with tailored account insights, ranging from flagging higher-than-normal automatic monthly payments, to reminding a customer to transfer money from savings to a checking account to avoid a possible upcoming overdraft.
Bank of America spends US$3Bn in technology development and acquisition, with about three times that on IT infrastructure maintenance.
Bank of America’s chatbot, Erica leverages two forms of artificial intelligence, including natural language processing to understand speech, text and intent, as well as machine learning to glean insights from customer data that can be turned into advice and recommendations. Bank of America is rolling out the first widely available Erica, to its 25 million mobile clients.
Among other things, Erica can send proactive notifications to clients about upcoming bills and payments; display key client spending and budgeting information and advice on ways to save; find new ways for clients to save more; manage credit and debit cards to help notify clients of card changes.
By integrating the AI assistant into their mobile banking app, Bank of America aims to free up their customer support centers, and enable them to deal with more complicated cases faster, contributing to enhanced customer experience and delight.
Citibank’s adoption of artificial intelligence is powered through their venture capital arm, Citi Ventures. Citi Ventures uses a strategic approach, that brings together startup investments with un-house innovation developed at its six Citi Global Innovation Labs.
The Citi Ventures portfolio includes 35 companies, whose expertise spans across and beyond financial services and technology, commerce and security, into machine learning and artificial intelligence.
Most recently, Citi’s Treasury and Trade Solutions entered into a strategic partnership with HighRadius Corporation, a software company specializing in cloud-based integrated receivables to launch Citi® Smart Match. By leveraging the proprietary artificial intelligence (AI) and machine learning (ML) technologies developed in-house at HighRadius along with its own proprietary assets, Citi will enhance the efficiency and automation of the cash application process of matching open invoices to payments received for its corporate clients.
Earlier, Citi Ventures made a strategic investment in Feedzai. Feedzai leverages deep learning to scan large amounts of data to recognize evolving threats and then alerts customers in real-time to protect against fraud.
The Bank of New York Mellon Corp has developed and deployed automated computer programs, or more than 220 bots across its businesses to build more efficiency and achieve significant savings.
The bank estimates that its funds transfer bots alone are saving it US$300K annually, by cutting down the time its employees need to spend on identifying and dealing with data mistakes and accelerating payments processing.
The internal, proprietary, voice-controlled AI Chatbot, Alexis to expected to help its IT employees manage enterprise storageand automate manual storage-related tasks.