In today’s altered financial environment, we are seeing new versions of credit scoring models which rely increasingly on machine learning to shape the future.
There are several instances where fresh graduates do not have enough credit history to secure loans from a lender. Often, individuals belonging to low-income families are categorized as ‘unbanked’, therefore making them invisible to the traditional credit scoring systems. That's where machine learning steps in.
Traditionally, credit scoring was based on a few factors: credit history and payment behavior. However, that is no longer the case because changes in the financial world have also changed the intricacies of risk assessment. Here machine learning acts as a vital tool that breaks large volumes of data into discernible patterns and insights that might have otherwise gone missing.
So, how does machine learning change credit scoring? What does it bring to the table, and what are its challenges?
Understanding Traditional Credit Scoring
Before diving into machine learning, it’s essential to understand the conventional methods of credit scoring. Traditional credit scoring models have long been the backbone of lending decisions, primarily using algorithms to evaluate an individual’s creditworthiness based on a limited set of criteria. The most widely recognised model, the FICO score, ranges from 300 to 850 and incorporates several factors, including payment history (35%), amounts owed (30%), length of credit history (15%), types of credit in use (10%), and new credit inquiries (10%).
While effective, traditional models often fall short for many individuals, especially those with limited credit histories or non-traditional financial backgrounds.
As financial institutions seek to expand their customer base and mitigate risk, there’s a growing recognition that traditional credit scoring methods need to evolve, giving way to innovative solutions like machine learning.
Machine Learning in Credit Scoring
Machine learning algorithms can process and analyse large datasets far more efficiently than traditional methods. By evaluating diverse alternative data points—such as social media behaviour, transaction histories, and even employment patterns—machine learning models can draw a more comprehensive picture of an individual's creditworthiness.
For example, a firm may apply machine learning to access the spending pattern and income pattern of a potential borrower, along with the traditional credit data. As a result, it is easier to predict whether the individual can repay a loan.
The result? More subtle credit evaluations may open doors for those who had otherwise been denied access.
How Does It Work?
Machine learning models learn from existing data to identify patterns that can predict how individuals will act in the future. Here’s how the process works:
- Machine learning models draw on an incredibly diverse set of sources, including traditional credit scores, as well as alternative data, like utility payments and rental history.
- Relevant features are chosen and converted into an analysis-friendly format. This might involve normalizing data, creating new variables, or removing outliers.
- Algorithms are trained on historical data so as to recognize patterns and correlations. This trains the model to understand which factors are most indicative of creditworthiness.
- The model is tested against another separate dataset so that outcomes can be adequately predicted. Further adjustments are then made based on performance metrics.
- Lastly, the model is deployed in real life after being validated to analyze credit applications.
Benefits of Machine Learning in Credit Scoring
The integration of machine learning into credit scoring comes with a plethora of advantages:
Inclusivity
By considering alternative data, machine learning can provide credit access to individuals who traditional scoring models might overlook. This can help millions gain access to loans, credit cards, and other financial products.
More precise
Machine learning capability can consider more variables and subtle patterns than the human eye, improving the accuracy of credit evaluations. This may help decrease the default rates for lenders while increasing the approval rate among borrowers.
Speed and Efficiency
Machine learning can dramatically accelerate the decision-making process by automating credit scoring processes. Applications can be processed faster, meaning borrowers have quicker access to funds.
Dynamic Adaptation
The models are continuously learning and adapting with time. With the increase in data being generated, the algorithms are more fine-tuned at making their predictions.
Real-World Applications
Many banks and fintech companies are already using machine learning in credit scoring. Upstart and ZestFinance popularised methods that extract risk from data sources not previously used.
For example, Upstart uses the factors it discovers through machine learning - about education or even employment history, to understand a specific borrower likely to predict the prospects of repayment for that particular borrower. Hence, they attain more loan approval than the traditional models.
Challenges and Ethical Considerations
Despite the promising advantages, there are challenges and ethical considerations that come with machine learning in credit scoring.
Bias in Algorithms
One of the most pressing concerns is the potential for bias in machine learning models. If the training data reflects existing societal biases, the model may inadvertently perpetuate these biases, leading to unfair credit decisions. Organisations must ensure that their data is clear of all biases and that algorithms are regularly audited for fairness.
Transparency
Machine learning algorithms can be complex and opaque, making it challenging for borrowers to understand how their creditworthiness is evaluated. This absence of clarity can lead to mistrust among consumers, especially those who are already vulnerable.
Data Privacy
The use of alternative data raises concerns about privacy and consent. Consumers may only sometimes be aware that their non-financial data is being analysed, which can lead to discomfort or distrust in the lending process.
Regulatory Compliance
As machine learning in credit scoring becomes more prevalent, regulatory bodies are beginning to take notice. Lenders must navigate the regulatory landscape carefully to ensure compliance with fair lending laws and data protection regulations.
The Future of Credit Scoring
The future of credit scoring will be evolutionary- especially since more sophisticated models powered by Artificial Intelligence and Machine Learning- with better predictive capabilities, will allow them to assess creditworthiness in real time.
Takeaway
The integration of machine learning into credit scoring is a significant step in the evaluation of creditworthiness. This step will better inform lending decisions and enable financial inclusion while empowering those who have been relegated to the periphery by traditional systems.
However, for its successful implementation, it is essential to oversee how these advancements are moved forward, committed to fairness and transparency in the interest of ethics.
Machine Learning can definitely pave the way toward an honest, fair and efficient credit-scoring mechanism for the credit lender as well as the borrower.