How Model Risk Management Strengthens Your Machine Learning Strategy

As adoption of artificial intelligence (AI) continues to proliferate around the economy, businesses must build strategies around how to manage these new processes. One sector in particular at the forefront of AI adoption is banking. Many banking processes are being assisted and replaced by artificial intelligence and it is revolutionizing their workflows. 

In addition to efficiency gains, AI integrations also provide personalized options for customers, leading to a more bespoke banking experience. But this burst of AI growth creates an environment that can quickly get complex, costly and laborious. As a result, Model Risk Management (MRM) will have to adjust to better align with this changing landscape to ensure new models are robust and fair, and banks are getting the most value out of their model development lifecycle.

We’ll give you a brief refresher on this topic and why it is so crucial to banking’s future, and then dive right into learning more about the future of Model Risk Management systems and their marriage with AI.

What is Model Risk Management?

Model Risk Management (MRM) is a process used to identify and manage the risks associated with using models. MRM seeks ways for companies’ models and algorithms to be more transparent so they can better understand how these models work and what could go wrong with them.

The MRM framework is especially utilized in highly regulated industries such as banking and healthcare to offer risk guardrails for practices such as automated credit decisioning and predicting patient outcomes among other business cases. The goal is to ensure that models are reliable and transparent, as well as, to establish a governance policy around a business’ model landscape. 

Building advanced models in an MRM framework 

The MRM process requires model developers to build with reproducibility and soundness in mind. Model building often consists of these steps:

  1. Identifying a business need: Understanding the business problem and establishing the need for a model
  2. Data collection: Sourcing and collecting the data needed for the model.
  3. Data preparation and analysis: Preparing the data for modeling.
  4. Algorithm selection: Developing the model using machine learning algorithms and techniques.
  5. Model Validation: Rigorously testing model performance across different scenarios. 
  6. Model Deployment: Running the model in a testing capacity for quality control.
  7. Model Monitoring: Monitoring the model’s performance and adjusting as needed.
Step 1: Identifying a business need

The first step in model development is identifying the business need. This collaborative process gives the model development team an understanding of what is trying to be achieved by the end business users. This crucial step allows the team to determine the most suitable approach to providing a solution.

Step 2: Data collection 

Data collection is the next step in model development. This step involves identifying, collecting, and storing data from different sources. 

Step 3: Data preparation and analysis

The third step is data preparation, which entails cleaning, organizing, and combining the data collected from different sources. This step in the process demands understanding the data, which often requires expert business and data literacy to produce meaningful results. You can streamline this process by using innovative data advisory services.

Step 4: Algorithm selection

The fourth step in model development is algorithm selection and tuning. Data scientists use machine learning algorithms and statistical analyses to build predictive models for various purposes, such as credit decisioning or prepayment prediction. These algorithms need to be optimized and tuned to achieve both effectiveness and stability.

Step 5: Model validation

Model validation occurs after a suitable algorithm has been selected and tuned. The process tests the model for several measures of reliability. Some examples include testing for bias across different features in the data, generalizability on new data, stability across time, and reliability for extreme values. The validation process occurs internally, as well as, externally for business-critical models. 

Step 6: Model deployment

The sixth step is model deployment, where the model is contained and executed in a server to be ingested into many different applications, such as a mobile app or loan operating system. This allows for scalable, rapid, low-cost predictions.

Step 7: Data capture and model adjustment

Lastly, after a model is deployed, it needs to be monitored for stability and accuracy. This is done by storing the data being fed into the prediction server and comparing it against the data the model was initially trained on. If the data begins to fundamentally differ in nature, the model in production will perform poorly, thus initiating retraining the model on newer data. A model left unmonitored could result in inaccurate predictions, which could have adverse outcomes. 

Benefits & challenges of AI models

Like most advancements in technology, there are tradeoffs. New algorithms and methodologies increase accuracy and speed, but may be difficult to interpret or explain.  As models become increasingly advanced and adopted, MRM processes need to adapt in unison. 


According to McKinsey & Company, AI models implemented within MRM frameworks can be beneficial by  “improving an institution’s earnings through cost reduction, loss avoidance, and capital improvement.”  Additionally, it can: 

  • Automate business processes such as credit decisioning or document preparation
  • Forecast capital scenarios given market volatility
  • Improve workforce efficiency by forecasting employee workloads
  • Increase credit approvals, making it more accessible

Businesses seeking to optimize decision-making processes should be aware that using machine learning models is not without its challenges: 

  1. The first challenge is the lack of data. Many data sets are not available to the public, making it difficult to build a model on authentic data.  
  2. The second challenge is transparency. It can be difficult for stakeholders to understand how algorithms make decisions and what factors they consider. Consequently, this can create tension among stakeholders who are interested in the decision. Users want to be able to understand the process and accuracy and transparency ultimately builds trust.
  3. The third challenge is built-in biases or inequalities in models and datasets, which unintentionally, yet disproportionately affects specific populations.
  4. The fourth challenge is model maintenance.  A vital but time consuming process that requires resources that businesses may find challenging to keep up with. 

The AI Evolution of Model Risk Management

Interest in incorporating AI into business processes has been on the rise, and rightly so. Evolving MRM operating models have exciting and valuable possibilities for companies focused on delivering affordable, accurate lending options. 

Incorporating MRM has the potential to improve the efficiency and accuracy of credit decisions, which streamlines the process. However, like any emerging technology, we must approach its implementation with expertise and diligence. 

Machine learning models can provide more accurate credit decisions, reduce the time it takes to make a decision, reduce human error, and increase efficiency. But they can also be biased, lack transparency, and have difficulty understanding complex situations.

At Lumos, we value the use of emerging technology but always put our clients’, businesses’, and communities’ needs first. We empower financial institutions to make sound lending decisions, allowing them to better manage risk and illuminate the path to sustainable growth with the help of actionable and insightful data

We are a trusted partner for revealing insights, solving problems, and facilitating agile growth. Schedule a demo to learn more about how we work with financial institutions to provide the data they need to make sound lending decisions.