The differences between AI vs traditional risk modelling are significant. Traditional models often struggle with complex, non-linear relationships. AI models excel at identifying these patterns, offering more accurate risk predictions.
Risk managers now face a choice. They can continue with familiar traditional methods or adopt AI-based risk modelling. This post examines both approaches, comparing their strengths and weaknesses as well as how to overcome the challenges of implementing AI risk modelling.
The case for AI in financial risk management
“The most fundamental building block of risk management—the risk model—could be hampering many organisations. Instead, many are now applying AI-based models to meet the demand for agility, accuracy, and equity.”
Ben O’Brien, MD, Jaywing
As Ben points out, traditional risk models have limitations—especially in volatile markets. They often rely on historical data and assume normal distributions. This makes them less effective when market conditions change rapidly.
AI-based models address these shortcomings. They can process vast amounts of data from diverse sources. This includes unstructured data like news articles and social media posts.
AI models also excel at handling non-linear relationships. Many financial risks are complex and interconnected. AI can capture these intricate relationships more effectively than traditional linear models.
Early adopters of AI in risk management gain a competitive edge. They can make more informed decisions and allocate resources more efficiently. This leads to better risk management outcomes and potentially higher returns.
Matthew Jones, Head of Retail Modelling at Nationwide says it best;
“Archetype clearly represents a powerful alternative in building credit scores. We were impressed by the ease of use, the rapidly-realised benefits and the ability to enforce intuitive behaviour on key variables in neural networks.”
Now, implementing AI is not without challenges. First things first, you have to invest in the right technology and skills. This is key because the technology you choose must also satisfy model interpretability and regulatory compliance. [Side note, this is something Jaywing does incredibly well.]
But what exactly are the key AI technologies transforming risk management?
Key AI technologies transforming risk management
It’s safe to say there are three key technologies at the forefront of transforming risk management:
- Machine learning
- Natural language processing
- Deep learning
Each brings unique capabilities to risk assessment and management, significantly enhancing the ability to predict, identify, and mitigate risks. Let’s briefly take a look at each:
Machine learning
In risk management, machine learning models can predict potential risks with high accuracy.
The beauty of machine learning models is their ability to analyse historical data to identify patterns. The models then apply these patterns to new data to forecast future risks. This allows you to take proactive measures to mitigate potential issues. The best bit is, as it learns it just keeps getting better over time.
Deep learning
Deep learning, a specific subset of machine learning, is particularly effective for complex pattern recognition. It uses neural networks with multiple layers to analyse data. This makes it well-suited for identifying subtle risk indicators in large datasets.
Deep learning models can process a wide range of inputs simultaneously. This includes market data, economic indicators, and company-specific information. The result is a more comprehensive risk assessment.
NLP
Natural Language Processing (NLP) is another crucial AI risk technology. In simple terms, it enables computers to understand, interpret, and generate human language. NLP can analyse unstructured data such as news articles, social media posts, and financial reports.
This is valuable for risk management because NLP can identify potential risks mentioned in text data that might be missed by traditional methods. It can also gauge market sentiment, which can impact risk levels.
These AI technologies can also work together to enhance risk management capabilities. And as they continue to develop, their impact on financial risk management will likely grow too.
Implementing AI-based risk management: a step-by-step approach
Implementing AI in risk management often requires careful planning and execution. Here is a step-by-step approach to help you to successfully integrate AI into risk management frameworks:
#1. Assess your organisation's AI readiness
The first step is to evaluate your organisation's current capabilities and needs. This involves:
- Reviewing existing risk management processes and identifying areas where AI could add value
- Assessing your data infrastructure and quality
- Evaluating your team's technical skills and identifying any gaps
Top tip: You can see what your current AI risk modelling maturity is using our AI risk modelling maturity model guide.
#2. Identifying high-impact areas for AI implementation
Not all areas of risk management will benefit equally from AI. Focus on areas with large volumes of data that are difficult to process manually, risk types that require real-time monitoring and rapid response, or processes where more accurate predictions could significantly improve outcomes.
Here’s a few examples of how Jaywing clients have applied AI:
- Nationwide was seeking to boost the performance of the application risk models it uses to approve existing customers for unsecured loans.
- Newcastle Building Society wanted to understand whether applying AI-based techniques to mortgage risk modelling could yield additional benefits.
- Secure Trust Bank needed to boost the predictive power of its models to make substantial reductions to bad debt.
#3. Data preparation and infrastructure setup
Of course, AI models are only as good as the data they're trained on. That’s why data management and preparation is so important. As a minimum we recommend:
- Collecting and centralising relevant data from various sources
- Cleaning and standardising data to ensure quality and consistency
- Implementing data governance processes to maintain data integrity
#4. Choosing and customising AI models
It’s important to choose AI models that best fit your specific risk management needs. Consider the type of risk you're addressing (credit risk, market risk, operational risk, etc.), the volume and variety of data you'll be processing, and the level of interpretability required for regulatory compliance.
#5. Integration with existing systems
AI models need to work seamlessly with your current risk management systems. This involves developing APIs to connect AI models with existing platforms, ensuring real-time data flow between systems, and creating user-friendly interfaces for risk managers to interact with AI outputs.
#6. Training and change management
Successfully implementing AI requires buy-in from across the organisation. Focus on training risk managers to understand and effectively use AI-powered tools and educating senior management on the benefits and limitations of AI in risk management. It’s also important to develop new workflows that incorporate AI insights into decision-making processes as well as establish processes for ongoing model refinement and performance monitoring.
Overcoming AI in risk implementation challenges
While AI offers significant benefits in risk management, its implementation is not without hurdles. Here are a handful of common challenges and strategies to overcome them:
Data quality and bias issues
Of course, AI models are only as good as the data they're trained on. To ensure high-quality outputs:
- Implement rigorous data cleaning and validation processes
- Regularly audit your data
- Use diverse data sources to ensure a comprehensive view of risks
- Develop protocols for handling missing or inconsistent data
Model interpretability and regulatory compliance
AI models, especially deep learning ones, can be complex and difficult to interpret. To address this:
- Choose models that balance complexity with interpretability
- Develop clear documentation of model logic and decision-making processes
- Work closely with regulators to ensure compliance with existing frameworks
- Implement explainable AI techniques to make model decisions more transparent
Collaboration between risk managers and data scientists
Effective AI implementation requires close cooperation between domain experts and technical specialists. To encourage this:
- Create cross-functional teams that include both risk managers and data scientists
- Establish clear communication channels between technical and business teams
- Provide training to help risk managers understand AI capabilities and limitations
- Encourage data scientists to develop a deeper understanding of risk management principles
Bottom line: By proactively addressing these challenges, financial institutions can smooth the path to successful AI implementation in risk management.
Measuring the impact of AI in risk management
When implementing AI in risk management it's crucial to measure its impact. To do this, we recommend several key metrics as well as benchmarking performance compared to traditional models.
Key performance indicators for AI-based systems
To assess the impact of AI in risk management, consider these key metrics:
- Prediction accuracy: Measure how accurately the AI system predicts various risk events
- Response time: Evaluate how quickly the system identifies and flags potential risks
- False positive/negative rates: Monitor the system's error rates to ensure reliability
- Risk coverage: Assess the range of risks the AI system can effectively monitor and predict
Comparing traditional vs AI-based risk model performance
To understand the value added by AI, it's also important to benchmark its performance against traditional methods. Here are a few ways you can do this:
- Run parallel risk assessments using both AI and traditional methods
- Compare the outcomes in terms of accuracy, speed, and comprehensiveness
- Assess the ability of AI systems to handle complex, non-linear risk scenarios
Embracing AI in risk management as a strategic imperative
AI technologies offer powerful capabilities that can enhance prediction accuracy, speed up response times, and provide more nuanced risk assessments.
However, implementing AI in risk management requires you to overcome challenges like data quality, model interpretability, and regulatory compliance–all critical considerations that institutions must address.
At Jaywing, we think AI-driven risk management is becoming less of an option and more of a necessity.
If you need help with your AI roadmap, get in touch, we’d love to help.