Two processes that drive an insurer's profitability, risk underwriting and fraud detection, are undergoing a rapid transformation thanks to artificial intelligence. Both share one trait: they are classification problems on large volumes of data, which is exactly where machine learning excels. Yet they also concentrate some of the most delicate ethical and regulatory dilemmas in the industry.
Fraud detection: beyond fixed rules
The traditional approach to fraud detection relied on predefined business rules: alerts when a claim exceeded a certain amount or met suspicious conditions. The problem is that fixed rules are rigid, generate many false positives and are easy to get around for anyone who knows the thresholds. Machine learning changes the approach by learning fraud patterns directly from historical data.
Three families of techniques are particularly useful:
- Anomaly detection: models that learn what a normal claim looks like and flag statistical deviations. They are valuable because fraud tends to be rare and constantly changing, which makes it hard to train supervised classifiers with many labeled examples.
- Graph analysis: organized fraud is rarely an isolated event. Representing policyholders, repair shops, physicians, lawyers and claims as nodes in a network makes it possible to detect suspicious communities, shared addresses or intermediaries who appear in an abnormal number of claims. Graph neural networks extend this idea by applying machine learning to the structure of the network itself.
- Pattern recognition: supervised models such as gradient boosting or neural networks that estimate the probability of fraud for each claim from dozens of variables.
The result is not to replace the human investigator but to prioritize their work: the system ranks cases by risk and focuses manual review where irregularities are most likely.
Automated underwriting and scoring
In underwriting, AI makes it possible to move from a slow, manual process to an agile decision flow. In life and medical expense insurance, traditional underwriting could take days or weeks while exams and documents were collected. Scoring models estimate an applicant's risk from available data and, in many cases, allow an immediate decision for standard risk profiles, reserving in-depth review for complex cases.
The operational advantages are clear:
- Speed: a drastic reduction in issue times, with a direct impact on customer experience and conversion rates.
- Consistency: more uniform decisions than those of multiple underwriters applying different criteria.
- Fine segmentation: pricing that more closely reflects each applicant's actual risk.
This progress ties in with the broader trend toward accelerated underwriting, which seeks to minimize invasive requirements without degrading the quality of risk selection.
The challenges: bias, explainability and data
Adopting these models is not without risk, and here the actuary has a central responsibility.
Bias and fairness. A model trained on historical data can reproduce or amplify existing discrimination. If certain variables act as proxies for protected characteristics, the model could penalize population groups unfairly or even illegally. Detecting and mitigating bias becomes a technical and ethical requirement, not an add-on.
Explainability. A coverage denial or a fraud alert must be justifiable. Complex models are black boxes that make it hard to explain why a decision was made, which clashes with regulatory expectations and with the customer's right to understand how they are treated. Explainable AI techniques, such as SHAP values, help attribute each decision to specific variables, but they do not eliminate the problem entirely.
Data quality and governance. No model can overcome the limitations of its data. Incomplete, mislabeled or biased information produces unreliable models. On top of that come privacy and personal data protection obligations, which are particularly sensitive in fraud and underwriting.
The role of the actuary
In the face of this technological wave, the actuary is no bystander. Training in risk, statistics and regulation puts the actuary in a privileged position to design, validate and govern these models. Organizations such as the Society of Actuaries, the Casualty Actuarial Society and the Institute and Faculty of Actuaries have promoted material on predictive analytics, ethical use of data and model validation, recognizing that actuarial judgment is indispensable for automation to be responsible.
The right balance combines the efficiency of the machine with human oversight: models that speed up and sharpen decisions, within a control framework that ensures fairness, transparency and compliance. Technology brings speed; the actuary brings judgment.
Conclusion
Artificial intelligence is already an operational tool against fraud and in underwriting, not a distant promise. Its benefits in accuracy, speed and consistency are tangible. But its real value depends on rigorous implementation: good data, explainable models and constant monitoring for bias. That is precisely the ground where the actuarial profession can and should lead.
References
- Society of Actuaries. Publications on predictive analytics and machine learning in insurance.
- Casualty Actuarial Society. Materials on predictive models and fraud detection.
- Institute and Faculty of Actuaries. Guidance on the ethical use of data and models.
- Wüthrich, M. V. and Merz, M. Statistical Foundations of Actuarial Learning and its Applications.