Credit: Data Science Central
“AI will be the most defining technology for the banking industry.”
-Ravi Narayanan, HDFC Bank
Such a comment, coming from an organization (HDFC) which has taken a gigantic leap in adopting conversational banking in the form Eva, India’s first AI bank agent, isn’t surprising. However, make no mistake, AI is being accepted globally as the new UI for banks to interact with their customers. Industry thought leaders increasingly agree that the power of AI will be transformational for banking.
AI has many other potential use cases across the banking industry. The following report is titled “Ten Use Cases for Banking.” We think these use cases could mature into potential disruptors for the banking industry at-large. Some uses cases are granular in nature so we would like to cluster them based on a segment of utility.
Operations and Intelligence:
A.Customer Analysis and Segmentation: AI tools allow retail banks to micro-segment customers into granular segments to offer highly personalized products and services to customers, thereby increasing overall stickiness and increasing overall customer lifetime value. The technology underpinning Robo advisors to help customers invest effectively is also being widely adopted. So these use cases can be described as:
A.1 Product Personalization – Offering personalized financial services and product bundles for each customer to the level of N=1 personalization
A.2 Robo Advisory Services: AI enabled advisors to suggest optimal product mix and bundles for maximizing investor returns
A.3 New Product Launches – Aggregate customer preferences and analyzes via AI to determine what new products customers are looking for
A.4 Differential Pricing – AI-powered analysis can help offer preferred pricing to customers based on total relationship or product mix
A.5 Lending Offerings – Machine learning can help offer tailored rates to customers based on their total financial picture
B.Customer Service: HDFC’s AI-powered bank agent, Eva, is an example of next-generation customer service and is a key use case of AI in banking. Benefits of these automated customer service platforms include faster response times, better customer satisfaction and reduced costs associated with customer service. In addition to facilitating better customer interactions, other benefits in this domain include more efficient data acquisition and better analysis of customer needs.
B.1. AI enabled chatbots for customer service: Chat agents like Eva will ultimately become the default platform for banks globally and will drive efficiency in resolving customer inquiries.
C.Process Automation: AI in complement to Robotic Process Automation (RPA) technologies is already mainstream across many back-office processes. AI is deepening the scope of RPA to go beyond plain rule and script-based automation in banking processes. Key use cases include but aren’t limited to:
C.1. Procurement Process Automation – A range of procurement processes across the banking enterprise is being automated using AI and RPA
C.2. Order management – Order management and processing at banks increasingly rely upon RPA and AI-based automation. Specifically, AI enabled smart OCR solutions are transforming paper form processing into digital formats.
C.3. KYC Processes – Different AI based process improvements and automation are currently underway across KYC processes in Banks.
C.4 HR processes – Several internal and external HR processes at banks have taken to using AI to automate processes like resume screening etc.
D. Governance, Risk and Compliance – Compliance related processes are incorporated into everything banks do. Technologies like AI, NLP and Vision form a spectrum of compliance technologies currently in action at banks. Some of the key areas in this domain include but are not limited to:
D.1 – Contract Management – Use of technologies like OCR, computer vision and machine learning can help automate the process of reading contracts, identifying key compliance needs and ultimately improve contract processing times.
D.2. – AML – Processes related to Anti Money Laundering can make smart use of AI and machine learning to flag abnormalities.
D.3 – Fraud Detection – Several aspects of fraud monitoring and detection will be offloaded to machine learning and AI technologies
D.4 – Risk Management – AI enabled risk management processes will be mainstream thereby automating or intelligently augmenting risk processes
E.Cost Optimization – AI can analyze data associated with various cost centers and help drive efficiencies by identifying overlaps and opportunities for streamlining
E.1 – Cost optimization via use of AI to enable smart savings
F. Customer Listening and Feedback – AI and machine learning can help gather customer feedback and assign urgency level, segmentation and action items
F.1 Customer Listening and Feedback – Providing smart channels via AI to listen to customers and fine tuning products and services.
G.Smart Payment Systems – AI is being used to identify users by the way they hold and use their phones e.g. how the user types or presses on the screen to access apps. Platforms like Alexa and Facebook Messenger are incorporating payment systems.
G.1 – Smart Customer Detection – Payment systems are using smart AI technologies to identify customers
G.2 – Use of payment mechanisms in AI enabled devices and messengers is a new channel for payments
H.Smart ATMs – The use of face recognition and iris recognition systems are making ATMs more efficient and secure
H1. Smart ATMs – Use of face recognition is transforming the ATM platforms
Banks must adopt AI across their enterprises to keep up with industry and government standards, satisfy customer preferences and drive efficiencies to maximize shareholder value.