natural language processing

Artificial Intelligence (AI) has become one of the most dominant enablers of digital transformation across a significant number of industries in recent years. With the application of AI technology including natural language processing (NLP), creating human-like chat bots has become easier. Today, a greater portion of India banks have switched over to Chat Bot technology for their customer on-boarding, queries, complaints, fund transfers, etc., due their proficiency in handling numerous queries with high response time and round the clock service. This case study is based on the virtual assistant of State Bank of India (SBI) – State Bank Intelligent Assistant (SIA), which is engaged in providing personalized service to users based on their historical preferences, search frequency etc., via analyzing the data with the assistance of AI techniques. The recent developments and emergence of Virtual Banking in India and the current trends in the modern banking systems are explained along with the features of SBI-SIA virtual assistant. As banking is reshaped by technology, financial stability is a key priority for the Bank of England. The Bank is engaging with FinTech companies to gather information and understanding on the financial stability risks that might emerge from FinTech developments. Such risks are expected from the explosion of technology-enabled financial services. It has now become common for many banks to integrate FinTech, machine learning and AI into their services because customers want more choices, flexibility and control over their banking. AI is a branch of FinTech but not every FinTech is AI. AI is machine intelligence. Machine learning and AI are often treated as synonyms. AI in retail banking is at its nascent stage in the UK, though the potential is extraordinary. In the UK, some banks have launched banking applications using voice recognition. The Royal Bank of Scotland (RBS) has decided to roll out its “Luvo” AI customer service assistant, powered by its partner firm, RBS Group, wider in its branches. Bank of America, Capital One, Société Générale and Swedbank are some of the banks that have experimented with chatbots.

Workers compensation claim processing is extraor￾dinarily inefficient and causes claimants and employers to suffer. In a typical Australian workers compensation insurer, the Claim Establishment Process can require up to 28 manual steps performed by claim adjuster. Claims departments follow strict business rules that stipulate if a claimant is eligible for certain claim benefits as well as the appropriate medical certificates, certificates of capacity, statutory reserves and claim flags to allocate to the claimant. These are often automated or semi-automated in complexity, however in practice, require considerable agentic decision-making to complete the processing. Claim adjusters perform these decisions, which can be expensive and introduce wait times that delay claimants and employers from receiving their benefits. This feasibility study presents a deep learning framework designed to automate the workers compensation claim process using generative AI. A proof of concept application, entitled ClaimGPT, was implemented using the OpenAI API and Salesforce Einstein GPT, providing agentic AI capabilities for claim generation and decision-making. These capabilities were combined with natural language processing models for claim textual data and image generation for claim documentation. The framework was implemented in two existing workers compensation insurers, focusing on the Claim Establish￾ment and Claim Decision Process. Both implementations were successful in accelerating the claim processing, while reducing the size of the claim reserves allocated to each claim established. The image generation models for claim documentation synthesised novel claim scenes and demonstrated claim simulation capability on a specific insurance use case.