Artificial intelligence impacts personalization for insurance through predictive analytics that can be used to tailor insurance offerings to individual policyholders

Asia’s insurance sector is at the front line of new era of wealth generation - the winners will be the insurers that have their digital transformation “house" in order

Automating claims processing right can help you reduce costs, improve outcomes, and assure patient and stakeholder satisfaction - here's how to think about it

The advantages of a holistic, member-centric approach to healthcare are clear - as are the impacts of the failure to achieve member centricity

Business simulation can provide both the experimentation and the guardrails helping insurance companies explore multiple paths to success

By focusing on transformation with a small “t” that addresses measurable performance gaps, health plans and administrators can use claims automation to control costs and risk

How the FICO Platform can enable explainable machine learning models for more effective PD calculations

Fierce competition, open banking and financial inclusion drive the uptake of transaction analytics in lending

Through explainable machine learning models, behavioural and PD models for the retail banking sector can be created with higher levels of predictiveness.

Whilst the EU have rightly focused on the use of AI and data science strategic leadership, more needs to be done on AI explainability

A key component of stopping fraud, waste and abuse is to focus on your most productive targets: Who or what should be the focus of your payment integrity program?

AI and machine learning have become popular concepts in payment integrity. But how can they help reduce your losses to health care fraud, waste and abuse?

Given the negative impact of fraud, waste and abuse on health care, it’s in every payer’s interest to focus on these problems. 

So how does a health care payer measure success in payment integrity? How much is enough?