Artificial intelligence in insurance is the use of machine learning, computer vision, and language models to price risk, process claims, detect fraud, and serve customers faster and more accurately than manual methods. The four highest-value applications are AI underwriting, AI claims processing, AI fraud detection, and customer service AI. McKinsey estimates generative AI alone could add up to $1.1 trillion in annual value to the global insurance industry, and around 90 percent of insurers are already evaluating or deploying it.

Open banking lets customers securely share bank data and payments through approved APIs with their consent. It powers account aggregation, lending, budgeting, and account-to-account payments under regulations like PSD2 and Section 1033.

A SaaS reporting engine is the system that lets each tenant generate reports from their own data on demand, without one tenant's heavy report slowing the product for everyone else. To build one, you separate reporting queries from the live application database using read replicas or a dedicated analytics store, enforce tenant isolation on every query, run report generation as asynchronous background jobs, cache repeated results, and deliver output as on-screen dashboards or exportable files such as PDF and CSV. The hard part is not the charts; it is isolating tenant data and keeping heavy queries off the path that serves live users.

KYC automation is the end-to-end digital process of verifying a customer's identity without manual review at every step. A workflow chains document capture and OCR, tamper and authenticity checks, biometric liveness and face matching, AML and sanctions screening, and automated risk scoring, routing only uncertain cases to a human.

A SaaS public API is the interface that lets external developers and partner systems integrate with your product programmatically. The core design decision is REST versus GraphQL. REST exposes resources at multiple URL endpoints using standard HTTP methods and is the safest default for public APIs because it is cacheable, predictable, and familiar to every developer.

AI medical image analysis development is the engineering of deep learning systems that interpret X-rays, CT scans, MRI, and pathology slides to detect disease and support clinical decisions. In the USA, every system influencing clinical decisions requires FDA SaMD classification.