Financial Services - Consumer Banking

Make Banking Safer, More Profitable, and More Personalized with AI/ ML

Digital Banks can achieve more automation, intelligence, and personalization with AI/ ML. Numerous banking activities are becoming invisible, as journeys often begin and end on interfaces beyond the bank’s proprietary platforms. For the bank to be ubiquitous in customers’ lives banks will need to reimagine how they engage with customers and undertake several key shifts by solving latent and emerging needs. The time to think about AI-first business models in consumer banking is now.

NLP can help banks to process huge sets of structured and unstructured documents and extract key information. Machine learning models help to explore the data and find patterns in crypto-currencies and NFTs. OCR can enable banks to automatically process hand-written documents like cheques, deposit forms, and others. Vision analysis can help to identify and authenticate new/old customers.

Speech recognition enables text extraction from speech and analyzes them; personal digital assistants can help provide wealth information, retirements plans, and more customized products to customer banking needs.

AI can help banks boost revenue by uncovering new and previously unrealized opportunities. It also offers more personalized services to lower operating costs by automating complex works, improving resource utilization, reducing error rates, etc. AI systems enable banks' processes to be agile by having seamless integration with non-banking apps, facial recognition linked payment, personalized offers, personalized money management, investment recommendations, and aggregated overview of daily activities. AI can help banks move from providing financial services to their customers to facilitating their financial betterment.

Use Cases

Fraud Detection

ML for Transactions Anomaly detection. Supervised/ unsupervised learning model to classify customer profiles, develop fraud scores, and assist in fraud investigation.

Intelligent Chatbots

NLP provides personalized answers to customers’ queries, understands purchase intention, and retrieves key information from the customer's unstructured answers

Personalized Share Trading Advice

Neural Networks help to retrieve key information from earning calls, company reports, understanding trading, and suggesting portfolios.

Intelligent Document Processing

Computer vision to retrieve key information from the large structured and unstructured documents, determine credit risk, the underline mortgaged asset risk.

Product cross-selling and upselling

Recommendation algorithms uses customer interactions, transaction/ investment behavior, analysis of customers demographics, financial standing, etc. to facilitate cross/up sell.

KYC/ KYB Automation

NLP helps to extract images, key information from the KYC documents and automated submission of those to banking software.

Want to know more about how AI can make your bank a next-generation digital bank?

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