When the humans are away, will the AI play? It’s anyone’s guess. JPMorgan Chase (JPMC), however, is more than a little hopeful they’ll put their man-made-brains to placing the best investment bets.
It’s true. People no longer make the risky calculations that big banks teeter on; we now use financial models to roll the dice for us. Financial models are mathematical representations of a financial asset or portfolio created by algorithms, coded theories and AI. Quantitative analysts (quants) rely on these models to predict performance and make market judgments based on historic and financial data.
When financial models take the lead on important trades and investments, a new type of risk to manage surfaces: model risk. What happens if the financial model is wrong or inaccurate? Whether it’s badly fed by low quality data, poorly calibrated, or just plain old misunderstood—for the biggest banks, a poorly performing model can cause damages that swell into the billions.
There’s an urgent need and opportunity for big banks to mitigate model risk to avoid financial disaster. But they can also earn the trust of customers and leverage this position for a competitive advantage.
>> continued on IBM Big Data and Analytics Hub here:
https://www.ibmbigdatahub.com/blog/jpmc-teams-ibm-data-science-and-ai-elite