A multi-layered approach to fraud can help to build robust fraud protection in your insurance organisation. Utilising the right technology can help to prevent and even predict fraud before it happens.
Learn how insurers are protecting their business with Kainos' Fight the fraud checklist.
Which of these areas are you utilising in your organisation?
1. Cloud secure
The cloud offers a secure technology infrastructure foundation for insurers.
- Comply with security and compliance requirements more easily and securely share and store sensitive data
- Innovate fast
- Increase efficiency & quickly implement technology
2. Digitise the claims process
Digital claims processes mean more efficient document processing.
- Fraud can be detected more easily by utilising AI & Automation technologies
- Extract information from documents more easily
- Verification of policies is more accurate
- Can create a self-learning system to manage claims at high speed while preventing fraud
- Support your staff in a more streamlined process
3. Utilising machine learning
Detect anomalies in large datasets to detect fraud faster.
- Alert teams to suspicious behaviour
- Faster than human capability to sift through large quantities of information
4. Fraud analytics
Use fraud analytics to risk-score transactions, prevent fraud and realise tangible benefit.
- Move away from an operating model that considers fraud as a post-transaction, investigative process
- Create a portfolio of fraud initiatives that deliver measurable benefit.
- Change the mindset of fraud as being after the fact and more about prevention.
- Justify fraud initiatives as a value driver, rather than as an operational cost.
- Integrate fraud analytics into your decision-making processes to inform transactions better.
5. Improve your customer experience
Use fraud analytics to improve the experience for most of your customers.
- Automate processes to reflect established trust.
- Focus on the benefits of making your experience better for most of your customers.
- Create exceptions processes for riskier customers based on analytics.
- Consider fraud when designing your customer experiences looking for opportunities to give customers the ‘benefit-of-doubt' rather than make processes more restrictive or lengthy by establishing trust on a transactional basis
6. Enterprise fraud taxonomy
Create a map, that defines potential fraud across your enterprise and act as a ‘north star' for your fraud strategy.
- Define specific fraud schemes
- Take each scheme in turn and quantify potential fraud
- Measure fraud against each item on the taxonomy to show benefit and value delivery
- Think about fraud at the enterprise level and not in silos, like claims, in order to prioritise fraud initiatives better.
7. Establish and source useful data
Build a data lake and data feature store to inform fraud analytics and create insight.
- Source 3rd party data that helps identify fraud.
- Build a data lake that collects a range of data. Transactional, meta, external.
- Collect simple data to inform simple fraud prevention. Keep lists of known bad actors.
8. Enterprise strategy
Enterprise fraud management (EFM) - multi-layer approach to fraud. This should look at a multi-year road map to prevent fraud.
9. Take the human out of the loop
Automate transactions, using machine learning to determine the riskiness of transactions based on previous decisions.
- Through progressive learning, analytics will establish the patterns in transactional data to help automate decision-making processes that rely on human intuition.
- Make decision-making processes more predictable and policy driven.
- Leverage your data to evidence and inform business decisions.
The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.
