Points of View
Timely and focused, offering our analysts’ unique perspectives on risk technology and our research.
EMS vendors must eliminate transaction fees and make multi-broker, multi-asset trading easier
Execution management systems (EMSs) profess to be multi-broker and multi-asset, yet traders are still juggling multiple EMSs on their desktops. There is a threshold effect at work, whereby EMSs’ penalizing transaction fees and workflow inefficiencies…
Firm foundation: aligning hardware and software is vital for effective AI
Financial firms neglect the hardware for AI tools at their peril. But even as chips and system architectures evolve, trade-offs remain. When it comes to hardware, firms need to know what to balance with what, to avoid being lumbered with fragmented IT…
Fraud-busting in the new ‘normal’: keeping costs and false positives down post-COVID
Fraudsters are profiting from the pandemic, while financial firms’ fraud-detection systems are swamped with false positives. As firms adjust to a new ‘normal’, graph analytics and supervised and unsupervised models can help them keep pace with criminal…
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…
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.
Smart thinking: mitigating renewables-linked price risk with neural networks
Failing to incorporate renewable energy sources effectively into power networks can create serious issues around energy pricing and forecasting. Some neural networks can mitigate renewables’ intermittency, but require the right expertise and data.
Is more data, and less math, a good thing in modern models?
Now that Big Data is mainstream, model developers face an epistemic trade-off: enable models to make more accurate predictions by loosening traditional statistical methodologies. But what impact might this have on the future accountability of our…
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…
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…
Bad sources? The risks of alternative data
New ways to capture and package previously inaccessible data have given financial institutions (FIs) a diverse set of methods with which to assess the creditworthiness of corporate and retail customers. Despite the appeal, however, deploying this data…
What price privacy as the value of transaction data soars?
Thanks to a booming payments market, the amount of transaction data is growing – as is its value. But regulation around it is patchy at best, and as more transaction data is used to feed models and analysis, more transparency and clarification around its…