Biometrics: assessing the opportunities and risks as regulations loom
Biometric technology can enhance fraud and anti-money laundering processes, but can carry big risks. As it becomes more widespread, financial firms and tech vendors must develop the security and governance frameworks to realize its potential – before regulators force them to.
Biometric ID solutions, increasingly seen as
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QuantitativeAnalytics50 2025 published
Chartis Research is happy to announce the inaugural QuantitativeAnalytics50 2025 ranking and report. This new report, a reimagining of and natural extension to our STORM research, examines how the computational cores of risk management, portfolio…
Reimagining Model Risk Management: New Tools and Approaches for a New Era
A collaborative report by Chartis and Evalueserve.
Insuring the weather: modeling the complexities of climate change
Extreme weather makes forecasting and quantifying insurance losses harder, and ‘cat’ models struggle to predict events more extreme than those in the past. As regulators demand more action, dynamic ‘earth system’ models offer a better way to anticipate more climate-change risks.
Balanced mobile data initiatives are vital in the COVID-19 fight
Governments need real-time data to help prevent the spread of the coronavirus, but may baulk at longer-term privacy battles. Workable options are possible, but officials must act now to enact them.
Breaking the doom loop: the danger of self-fulfilling prophecies in modern credit risk
The ability to distribute trustworthy credit is a societal cornerstone. But what happens when traditional credit scoring methodologies aren’t available? Will new 'advanced' credit models in emerging markets be self-fulfilling prophecies?
Meaning is everything: the problem of defining ethics for AI algorithms
Developing AI algorithms without strict definitions could create ethical problems for financial firms. To avoid mishandling their algorithms and potentially harming certain customer groups, firms must ensure their AI tools are no broader than the definitions they are based on.
Why more is not always better in the world of credit
Advanced analytics and AI promise revolutions everywhere, but real-world constraints abound. This is notably true in the world of credit scoring, which needs to be understandable, is often slow to give out real-world results, and can be muddied by the addition of unnecessary factors.