Navigating Custom Models in Financial Analytics

In today’s dynamic financial landscape, institutions are increasingly turning to custom models to gain a competitive edge, mitigate risks, and make data-driven decisions. Custom models offer the flexibility to tailor solutions to unique needs, providing deeper insights and more accurate assessments. In this article, we delve into the intricacies of custom models, exploring their development, deployment, and the crucial role they play in anticipating financial trends and ensuring effective decision-making.

Lending decision models can be tuned to allow a decisioning platform to provide insights into more than just income, expenses, identity, and balances, but also assess loan affordability, incremental risk, and other metrics that have been traditionally difficult to measure. By integrating bank transaction data with credit bureau data and scores, as well as its own internal data about customer behaviors, a lender can build its own custom scores to make the best lending decisions possible. ¹

The Art of Custom Models:
Tailoring Solutions to Unique Needs

One size does not fit all in the world of finance. Every institution has its own set of challenges, objectives, and data nuances. Custom models allow organizations to fine-tune their analytical tools to address specific requirements, whether it’s optimizing credit risk assessment, forecasting market trends, or enhancing operational efficiency.

In a recently published article, Tech Bullion highlights ‘recalibrating risk management frameworks’ and models as being pivotal, to maintaining a success in the future of financial uncertainty. Moreover, there are several key factors that must be taken into account, with regards to keeping your business strategy ahead of the curve. Prateek Khandelwal from Tech Bullion writes, “As the potential for a recession looms, financial institutions will benefit from holistically recalibrating their risk management frameworks to weather the storm. This approach considers all aspects of risk management, including credit, operational, market, and interest risk, diversification of portfolios, regulatory compliance, capital adequacy, and loan loss reserves. It’s essential to manage each component to incorporate enhanced monitoring and oversight guided by data-driven decisions and an early-warning system to avoid a reactionary mentality.”⁽²⁾

Several key players such as Goldman Sachs, American and Express, and JP Morgan & Chase have adopted “Big Data” Strategies when it comes to ensuring model effectiveness and creating effective model monitoring strategies. To read the full breakdown, read the article from Turing that explores, “7 Real-world examples of financial institutions using big data.”⁽³⁾

Mastering Credit Modeling:
Techniques for Accurate Assessment

Credit modeling lies at the heart of risk management for financial institutions. By leveraging custom models, banks can refine their credit assessment processes, incorporating a myriad of factors to better predict borrower behavior and default probabilities. Advanced statistical techniques, machine learning algorithms, and alternative data sources empower institutions to make more informed lending decisions while minimizing exposure to risk.

When it comes to, “what should companies monitor for healthy ML models?” there are several variables to keep an eye on⁽⁴⁾:

Reality vs prediction: compare the predictions of ML models with real world data to determine whether the model’s predictions are accurate or not. If there is a large gap between the two, it means that the ML model needs to be updated.

Data distribution changes: The world sometimes changes as fast as the Covid pandemic. Such circumstances lead to a huge shift in the data distribution. The data distribution is a reminder to consistently monitor and update your ML model.

Error free data: ML models require high quality data to perform optimal analysis. Regular data cleaning can ensure the quality of the data.

Fairness: If the ML model discriminates against one or more ethnic, religious, or other groups, it must be averted.AI biases can have serious consequences for the company’s market value if they are uncovered.

Operational Metrics: It is useful to check the usage of CPU, memory, hard disk and network I/O. If these are close to full capacity, maintenance is required for an effectively working ML model.

Predictive Models in Action:
Anticipating Financial Trends

Predictive models offer a glimpse into the future, enabling institutions to anticipate shifts in the market, customer behavior, and economic conditions. By harnessing historical data and advanced analytics, custom predictive models can identify patterns, correlations, and anomalies, helping organizations stay ahead of the curve and capitalize on emerging opportunities.

Predictive Analytics Paving The Future

Investopedia defines Predictive Analytics as, “The use of statistics and modeling techniques to forecast future outcomes.”⁽⁵⁾ Financial lending institutions use predictive analytics to fine-tune their operations and decide whether new clients are worth the investment. Investors use predictive analytics to decide where to put their money.

Predictive Analytic Financial Models

Underwriting and Credit Scoring both use facets of Predictive Analytics to help financial institutions make better financial decisions when it comes to lending. A common use case is when a consumer or business applies for credit, data on the applicant’s credit history and the credit record of borrowers with similar characteristics are used to predict the risk that the applicant might fail to repay any new credit that is approved.

Another emerging trend in the financial space is the use of Synthetic Data. As defined by Forbes, “Synthetic data, essentially a fabricated dataset generated from existing data, mirrors the statistical properties of real-world data without compromising individual privacy.”⁽⁶⁾ Recently, there have been major strides taken to include synthetic data with Fraud Detection and Prevention, as well as Credit Risk Modeling. Whereas synthetic data can assist fraud detection models by incorporating a larger dataset of transactional scenarios, including anomalies that may exist undetected in traditional models.

As previously examined in this blog post, there are a myriad of factors that exist within credit risk management. Synthetic data can provide guidance for how to handle these factors via creating real-world simulations on a variety of economic scenarios. The main goal by running simulations through synthetic data is to test the resiliency of your existing credit risk models.

Synthetic Data elevates Custom Model development for Financial Analytics and is a trend to watch.

Ensuring Model Effectiveness:
The Importance of Model Monitoring

Creating a custom model is just the beginning. To maintain relevance and reliability, ongoing monitoring is essential. Model monitoring involves tracking performance metrics, validating assumptions, and recalibrating algorithms as needed. By continuously assessing model effectiveness, institutions can detect potential biases, mitigate risks, and uphold the integrity of their analytical frameworks.

It’s no secret that artificial intelligence (Ai) is here to stay in financial services. However, Ai has not been met without its flaws. As examined in an article recently published by Biz Tech Magazine, “AI models are built and trained by humans. As a result, they can have the same blind spots that people do. Take lending, for example: An AI model can be a boon to a financial services company seeking insights about whom to lend money and at what interest rates, but if the model includes discriminatory factors, then it will screen people out, or set unfair loan pricing levels based on those factors.”⁽⁷⁾

One company aimed at elevating the future of compliance in model risk management is Fairplay. Fairplay, as the world’s first ‘Fairness As A Service Company’ highlights the importance of addressing bias in alternative data sources and predictive models, with cash flow underwriting being recognized as a fairer form of underwriting.⁽⁸⁾

GDS Link’s Rich Alterman recently had the chance to meet with Kareem Salah, CEO of Fairplay to discuss what measures companies are taking to ensure fairness in lending practices.

In the podcast episode, ”Lending Link LIVE: CEO of FairPlay Discusses the Future of Compliance in Model Risk Management. – GDS Link”, Kareem Salah explores how, “The expectation of the public is that if you’re going to use alternative data, if you’re going to use predictive models, that you’ve got to grapple with the very real tendency of those systems towards bias.” Kareem deduces Fairness in Lending down to 5 main questions that his company, Fairplay, asks clients who plan to use predictive models: “Is my algorithm fair? If not, why not? Could it be fairer? What’s the economic impact to our business of being fairer? And finally, did we give our declines, the folks we rejected, a second look to make sure we didn’t say no to somebody we ought to have approved?”

You can check out the full episode on your preferred platform HERE

Model Governance:
Maintaining Transparency and Compliance

In an era of increasing regulatory scrutiny, model governance is paramount. Financial institutions must establish robust frameworks for model development, validation, and deployment to ensure transparency, accountability, and compliance with regulatory requirements. Proper governance practices help mitigate risks associated with model errors, biases, and misinterpretation of results.

ESG Compliance has become arguably the most important emerging governance measure implicating financial services around the world, today. Harvard defines the goal of ESG to, “ensure that organizations consider material, non-financial environmental, social, and governance factors that affect financial performance alongside more traditional financial metrics when making business decisions.”⁽⁹⁾

PWC, one of the Big 4, highlights ESG as being one of the larger trends of 2024 saying, “Across issues, business leaders are shifting strategies to account for the ever-growing array of risks, and boards need to parallel that shift with a fortified focus on operational resilience, integrating strategy and risk governance.”⁽¹⁰⁾

Back in 2022, GDS Link examined the effect of ESG on consumer behavior that examined surveys conducted by Zelros and Connectd. We found that younger entrepreneurs are most committed to issues relating to sustainability, social and governance. That the vast majority would reject funds from investors who do not meet their criteria. Lastly, more than half would have serious concerns about establishing any sort of partnership with investors whose environmental footprint could stand to be significantly improved.⁽¹¹⁾

From Concept to Deployment:
The Journey of Custom Model Development

The development of a custom model is a multifaceted process that begins with understanding the business problem and defining the scope of analysis. It involves data collection, preprocessing, feature engineering, model selection, validation, and testing. Collaboration between domain experts, data scientists, and stakeholders is crucial at every stage to ensure alignment with business objectives and user requirements.

A large component of Custom Model Development comes down to difference in use cases when using Alternate vs. Traditional Scoring Models

Traditional and alternative credit scoring models aim to achieve the same purpose; the difference is the criteria they use to determine a borrower’s score. Credit Infocenter explained that a traditional credit score is usually determined strictly by a borrower’s lines of credit. Credit scoring agencies will want a mix of information to determine a credit score, including:

Payment history – has a borrower ever missed a payment or defaulted on a loan?

Current debts – can a borrower handle another payment?

Credit utilization ratio – does a borrower have a diverse borrowing portfolio?

The number of open accounts – how has the borrower handled making multiple payments?

Length of credit history – older accounts show a better payment history.⁽¹²⁾

Regardless of how you organize alternative and traditional data, the key features will be the same. Credit scorecards for new accounts typically utilize four to five variables. These variables usually include credit scores from third-party credit bureaus such as delinquency, failure, fraud, payment ray, and rating codes. Some institutions use dozens of variables, while some like to keep it simple. Other data include payment experiences, public records, and financial and firmographic information.⁽¹³⁾

Credit Risk Scorecard Models Help Define: Credit limit management, Risk assessment of credit applications, Propensity to buy or churn, Early alert systems, and Cross-selling of additional products to the best risks in the portfolio.

To better understand model development workflows, check out the diagram from PWC below. ⁽¹⁴⁾

For more information you can Check out how GDS Link is using Cloud Analytics to help facilitate accurate model monitoring: Cloud Analytics – GDS Link

Maximizing Model Performance:
Strategies for Enhancing Accuracy

Optimizing model performance requires a combination of technical expertise, domain knowledge, and iterative refinement. Techniques such as ensemble learning, feature importance analysis, and hyperparameter tuning can enhance accuracy and robustness. Regular performance evaluations and benchmarking against alternative models help identify opportunities for improvement and innovation.

Check out How GDS Link develops Credit Scoring Models with Machine Learning to increase approvals & reduce default with ML scorecards, ultimately helping to accurately predict credit risk and automate scoring for instant decisioning.⁽¹⁵⁾

Smooth Deployment:
Best Practices for Custom Model Implementation

The successful deployment of a custom model hinges on effective integration into existing workflows, systems, and decision-making processes. Institutions must consider factors such as scalability, interpretability, and usability to facilitate adoption and acceptance by end-users. Comprehensive documentation, training programs, and post-implementation support are essential to ensure a seamless transition and maximize the value derived from the model.

Learn more about GDS Links Credit Scoring Models in this recent case study with Go Easy. “By using the PMML tool, we were able to reduce time to implement by 50%, from about 20 hours previously to 5-10 hours and with better accuracy because the process reduces the risk of manual errors,” Zaur Akhmedov.⁽¹⁶⁾

Conclusion:

Custom models represent a powerful toolset for financial institutions seeking to navigate uncertainty, capitalize on opportunities, and manage risks effectively. By mastering the art of custom model development, deployment, and governance, organizations can unlock new insights, optimize decision-making, and drive sustainable growth in an ever-evolving landscape.