Nov 3, 2025
Adoption of machine learning and AI for risk modelling
A take into how advanced analytics techniques are changing risk management.
Risk management is going through a silent revolution. What once relied on statistical models and expert judgment is now driven by algorithms that learn and adapt on their own. Banks and insurers are leading this transformation, but they’re not alone: according to studies by McKinsey and S&P, two out of three large companies already use machine learning models to prevent and mitigate risks such as fraud and default.
Advanced analytics and AI are redefining how we understand and manage risk.
For companies, anticipating negative scenarios and making forward-looking decisions is essential.
A risk model works like a radar, it helps detect potential adverse scenarios and make decisions to either minimize the likelihood of them happening or prepare in case they do.
In the financial sector, after the 2008 global crisis, models used to estimate losses associated with credit risk became both a regulatory and business cornerstone. The results from predictive models enable institutions to seize market opportunities with greater confidence and to make decisions that help them better withstand future crises.
The decisions made using risk models are extremely sensitive. What techniques are used to ensure they’re reliable?
Here’s where we get a bit more technical.
Statistics is the science of using data to explain the behavior of variables in terms of probability and to make decisions based on likely adverse scenarios. Traditional methods such as linear or logistic regression aim to fit data to distributions and behaviors defined by mathematical functions and statistical assumptions. In simpler terms, we assume the relationship between our variable of interest and the variables that explain it follows a certain shape.
These models have been widely used because they are relatively easy to interpret, and because their underlying assumptions capture dynamics that are commonly observed.
However, they have limitations when it comes to identifying more complex patterns and providing estimates that reflect them.
Interpretation, the biggest challenge when adopting techniques that capture much more complex patterns.
It’s becoming increasingly common to hear about machine learning. In fact, many people refer to it without even realizing it. The algorithms behind social media recommendations, streaming platforms, or even the text generated by ChatGPT are all examples of predictive models built using these techniques.
Unlike traditional statistical models, machine learning algorithms don’t rely on predefined forms or relationships. They are trained to identify patterns in data, and their predictions are based on those patterns. This makes them highly adaptable when capturing complex relationships among many variables.
If the goal is to fit historical data more accurately and make better predictions, machine learning techniques excel. But what happens when we can’t explain their estimates? When reverse-engineering their logic is nearly impossible? That’s one of the biggest challenges in applying them. This “black box” nature, sometimes making them nearly indecipherable, doesn’t sit well with regulators or decision-makers, especially in traditional sectors like banking and insurance.
Still, given how critical risk models are for key business decisions, the predictive power of machine learning algorithms is pushing all industries, including financial services, to adopt them. And regulators are learning to adapt to this new reality.
The future of risk management in the era of machine learning and generative AI.
Complex dynamics, unexpected crises like the COVID-19 pandemic, and the current revolution of generative AI are driving organizations to adopt new approaches for their risk models and integrate them into business ecosystems to enable faster decision-making.
Machine learning models are gaining ground in highly regulated sectors like banking and insurance, following the European regulator’s guidelines that allow them to coexist with traditional methods, provided they meet requirements for explainability, validation, and human oversight.
Meanwhile, generative AI has the potential to accelerate model implementation and validation through agents that can query training data and model outputs.
There’s a lot of work ahead.
Do you want our consultants to help you with this?
Reach out to us.
Felipe Uribe Velásquez
Director
Camilo Monsalve Maya
Data Engineering Consultant
