Until a few years ago, credit scoring was calculated based on the customer’s credit data, such as loans, credit card payments, and mortgages.
It did not include the population, such as people with no income sources or immigrants who had little or no credit history. This became a disadvantage for a lot of people who were seeking credit. They could not prove their financial reliability without a credit score.
However, financial institutions like fintech companies and traditional banks recently noticed this gap.
They have started considering other data sources, such as timely payment of utility bills, purchase history, rental history, and social media activity, to determine a customer’s financial reliability.
In fact, they have gone a step further.
To accurately assess customers' creditworthiness and gain a holistic view of their finances, financial institutions are turning to artificial intelligence (AI).
AI is helping financial institutions in:
- Gathering alternative data about the customer from different sources.
- Analyzing the customer’s current economic condition using real-time data.
- Streamlining the underwriting process and empowering the underwriters to make quick decisions.
- Processing documents and responding to customers quickly.
- Segmenting customers and tailoring products and offers to meet their expectations.
While AI has made credit scoring and seeking credit more inclusive, it is not devoid of bias. After all, as Deloitte observed, AI systems are only as good as the data they are fed.
The Bias In AI-Based Credit Scoring and Customer Segmentation
There are many ways bias creeps into the AI-based credit scoring models and customer segmentation.
- The input datasets could sometimes carry gender, socio-economic, and racial biases. Some datasets are also incomplete or do not represent a population enough.
- Due to insufficient regulations, emerging economies lack transparency in data usage and handling.
- The historical bias creeps into the existing models while training the algorithms.
- As the AI models self-learn, they may learn new biases that could impact decision-making.
Financial institutions must build a bias-free AI model to give customers equal access to credit.
Let’s find out how to do that.
How to Build a Bias-Free AI Model for Credit Scoring and Customer Segmentation
A bias-free AI model can help financial institutions improve financial inclusiveness, build customer trust, improve transparency, and avoid reputation damage or punishments due to bias and discrimination.
The following steps can help financial institutions build a bias-free AI model:
- Collect more diverse data to train AI models
Financial institutions must look beyond historical datasets to train AI models. That’s because historical datasets are inherently biased. For example, women and racial minorities were underrepresented historically, due to which the AI models could turn out to be biased. Financial institutions can gather more data sets to build a fairer AI model. But, if left unchecked, AI could self-learn and reflect bias in its decisions. That’s why it’s essential to collect more data from diverse sources that adequately represent the target demography, train the AI models to identify discriminatory patterns, and alter the data to make the model fair. This will enable the AI to self-identify the bias and adjust the algorithms for a bias-free outcome.
- Focus on building a fair AI model
Typically, AI models are built to generate accurate results and mimic human processes. However, developers must look beyond these usual capabilities to create a fair AI model. They can begin by taking the ethical and discriminatory issues into consideration from the designing stage, training the model from the beginning, and prioritizing fairness and transparency along with accuracy.
- Validate the test data thoroughly
An AI model cannot detect bias when financial institutions use biased training data to test it. They need unbiased testing data from various sources to help the model identify discriminatory patterns. Additionally, as data volume increases, there are chances of biased data entering the AI system. Hence, companies must monitor the system continuously and perform rigorous tests in different scenarios to make it bias-free.
- Address the biases
Despite thorough testing, an AI model could still carry bias, unfairly disadvantaging specific customer segments. For example, an AI model could differentiate between zip codes and decline mortgages to certain ethnicities belonging to those zip codes. That’s why many financial institutions have started investing in adversarial AI models. These AI models are trained to look for patterns and biases in the original AI model and flag them for correction. Instead of manually adjusting the parameters, the adversarial AI re-configure the AI model to accept more variables and reduce bias. An approach like this helped reduce the mortgage approval rate gap for some ethnicities residing in a particular zip code by 70%.
Conclusion
The early movers in the banking and finance industry may have already adopted AI to segment customers and score their creditworthiness. But there’s always an inherent risk of bias creeping into the AI model by those building it. AI alone cannot eliminate bias. Human intervention is essential to make it inclusive.
Training the AI models with diverse datasets and continuous testing will help financial institutions improve the quality of credit score models and enhance customer experience. They can unlock new opportunities and grow to the next level by serving more customers.
However, to make sense of the diverse datasets and build an inclusive AI model requires domain expertise and a deep understanding of the industry.
That’s where Wissen can be of help to financial institutions.
We can unlock insights from an enormous volume of unstructured customer data and use our expertise in machine learning (ML) to build customized and contextually relevant AI models. This will help financial institutions reach more customers and offer tailored credit products and services.
To know more about our AI and ML capabilities and how we can help you build a bias-free model, contact us.
