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The general health of a country’s credit economy is always of great concern. No country can flourish if the majority of its subjects remain trapped in a cycle of poverty. Lack of accessible formal credit is known to have a big role in perpetuating systematic economic barriers, inter-generational poverty, and class immobility.
Preface
The general health of a country’s credit economy is always of great concern. No country can flourish in the majority of its subjects remain trapped in a cycle of poverty. Lack of accessible formal credit is known to have a big role in perpetuating systematic economic barriers, inter-generational poverty, and class immobility.
In the financial world, “Credit” often refers to an agreement to receive something of value now with an explicit promise to repay the same in the future. Credit enables entities (individuals and businesses) to get immediate access to the tools they may need (like education, machinery, etc) to enable them to produce better outputs in the future (like providing jobs to others).
Access to credit is considered to be one of the most important pillars for the economic development of a country: it increases competitiveness, creates job opportunities, eradicates poverty, builds wealth, generates assets, promotes flexibility, and fosters inclusive economic growth. Most importantly, it enables investment in human capital and businesses, and it has the potential to reduce inequality in society and drive economic growth.
The credit life cycle begins when a potential borrower approaches a lender for a credit advance. From the lender’s perspective, the decision on whether to grant the loan or not is determined by the potential profitability and risk of the transaction. The lower the risk and greater the profitability to the lender, the more they would be willing to extend the loan.
Before deciding on the lending viability, lenders seek to gauge the credibility and repayment capacity of the borrower. Every lender (bank or lending institution) has its own internal norms and procedures for underwriting the loan and scrutinizing the applicant’s details and credentials. This process of assessing the lending viability is better known as “credit appraisal”.
To know the previous credit profile of a borrower, the lender considers the credit score and a detailed credit report procured from an established credit bureau. A credit bureau is an agency that collects and researches the lifetime credit information of the borrower and shares it with the lender.
This credit score forms an important component of the credit appraisal process performed by the lender. If the credit appraisal results are favorable, the lender will lend to the borrower, and if not, the lender will refuse to grant the loan.
Traditional credit scoring agencies had been providing great predictive outcomes to lenders for many years. Some of the underlying factors, which they used to determine credit scores included the borrower’s repayment history, current debt, amounts owed, type of debt, credit history, frequency of credits, and payment interest. Each credit-checking bureau has its own proprietary algorithm to assign a credit score to the borrowers.
However, the traditional credit scoring process followed by most bureaus has historically favored only affluent borrowers while leaving the less well-off borrowers with no way to obtain the loans. Traditional lenders have limited ways of assessing the credit worthiness of vulnerable groups like the poor, women, and small businesses as these groups often do not have any tangible data or credit history. This leads to a vicious cycle because without a loan, they cannot build a credit score, and without a credit history, they cannot avail any loan… the classic chicken-egg problem.
This has exacerbated the already wide supply-demand gap for credit products and its access. On the one hand, lenders were not able to advance loans due to the lack of visibility on the applicant’s credit score. As a result, their loan approval ratio and profitability had dipped substantially. On the other hand, many applicants had to face rejection as they were new to the credit market and didn’t have a lot of background data to assess their true repayment capacity.
This explains why lack of data has historically been an obstacle for banks and financial institutions to extend credit to the unbanked, and thus, an impediment to tapping opportunities at the bottom of the pyramid and achieving financial inclusion.
Also, with the changing times, the way people look at credit and financing has undergone a sea of change over the last five years or so. The hitherto credit scoring techniques have not been able to match this radical shift in their way of assessing the creditworthiness of a potential loan applicant. Hence, the older credit scoring process is now slowly becoming outdated, making way for new and innovative ways of scoring applicants.
The Rise of Alternative
Credit Scoring
In order to overcome the aforementioned limitations of the traditional credit scoring approaches, observers have proposed that lenders could possibly cast their nets wider and look for more data points, which could give them a holistic view of their borrowers. This methodology is referred to as “Alternative credit scoring” in the contemporary financial world.
Alternative credit scoring is a more inclusive credit scoring mechanism, which goes way beyond the traditional parameters employed by credit bureaus like Experian, FICO and CIBIL – and leverages many more data sources to assess the borrowers’ current financial standing and willingness to repay in order to get a more holistic credit risk assessment.
The biggest beneficiaries of alternative credit scoring mechanisms are borrowers who are new to the credit and financing ecosystem. For such new borrowers, there is no sufficient centralized data available, but this doesn’t imply that they cannot avail credit. New-age alternative credit scoring companies use other tangible factors like a digital footprint to determine the credit-worthiness of a new customer.
This provides benefits at both ends. By democratizing access to credit, borrowers who are new to the credit ecosystem can still avail loan facilities irrespective of their lack of credit scoring data on traditional channels. Lenders also can utilize alternative credit scoring in order to boost their penetration in previously unexplored territories while still keeping their risk minimum.
At the core of the alternate credit scoring companies’ competencies are three key factors – the ability, intent, and stability of the customer to repay the loan measured on the basis of these innovative scoring systems.
Alternative credit scoring demonstrates the potential strength of combining data from multiple sources like airtime usage, mobile money usage, geolocation, bills payment history, and social media usage.
Alternative data can take various forms, ranging from data based on observations of the borrower’s operations to data relating to the business principal’s personal risk characteristics and credibility.
The various sets of data that can be potentially used to determine an entity’s alternate credit score (ACS) include:
With machine learning techniques that are currently being used to perform trend analysis and default prediction, transactional data is becoming a promising type of alternative data for credit scoring. Opportunities to acquire transactional data are being created by open banking and OpenAPI initiatives while financial institutions can also source transactional-based data from third-party data providers.
Machine learning can be used to pick up the borrowers’ micro patterns. For example, raw call detail records can be transformed into behavioral patterns to correlate with risk, ultimately providing lead generations for financing companies. The use of machine learning can give banks better insights, increase their sales through improved credit approval rates, reduce bad debt through better exposure management, and minimize processing time through automated decisions.
Multiple components are needed to bring alternate data models to life. Consumer consent and collaboration-based models will be the de-facto standard in the new world.
Financing companies that want to reach the “thin file” consumers should make faster, more reliable decisions with deeper insight and data points. It is estimated that formal credit scoring models generally use about eight to 10 variables. Meanwhile, alternative data credit scoring has the capacity to use more than 500 data points.
Banks hoping to use alternative credit scores to make lending decisions need to perform a series of steps. First, the shortlisted alternative data points need to be collected from various third-party data providers as demonstrated in the earlier sections. The collated data then needs to be pre-processed, so that relevant data points are extracted that can be used to run the ML models. Based on the results of the ML models, the prediction results of alternative credit scoring are determined. Finally, the decisions about loan application approval can be taken, for which a workflow needs to be developed.
Source: Data for alternative credit scoring – HK Applied Science and Technology Research Institute (ASTRI)
To support decision-making about loan applications and satisfy risk management requirements, there are three phases involved in developing a machine learning model for predicting default.
Broadening the data universe can be useful, but it also adds to model complexity. Once the model needs to compare variables consisting of numbers and characters (alphanumeric), which may have discrete or continuous distributions, it becomes important to decide which model generates the most accurate predictions of the probability of default.
In practice, the best machine learning algorithm will depend on the problem that needs to be solved. All machine learning algorithms have their respective pros and cons as alternative credit scoring models.
The input dataset is usually split into a training and a testing set. The usual rule for splitting the data is 70% for training and 30% for testing (or 80% and 20%, respectively) based on the Pareto Principle.
Source: Data for alternative credit scoring – HK Applied Science and Technology Research Institute (ASTRI)
A machine learning model that rejects too many loan applicants may, for example, not allow the bank to deliver enough of their products. On the other hand, if the number of True Positives is large, the bank may not have enough staff to handle the cases individually. In conclusion, an alternative credit scoring model needs to perform well both quantitatively and qualitatively. The right threshold needs to be determined by taking the perspectives of both data scientists and business managers into account.
By comparing the importance of all the features, a subset of features can be selected to replace the original training dataset. There are three advantages to applying feature selection:
To facilitate the automation of the workflow for alternative credit analysis, an online lending platform is needed to manage the steps involved in the process. These steps include the structuring and categorization of data fields, analysis by machine learning, decision making, and continuous monitoring. An online lending platform can achieve shorter turnaround times for loan approvals, which, in stormy economic times, can be critical in helping entities to survive. It can also help lenders’ operations become more cost-effective in the processing of loan applications.
Human discretion is involved only when the final decision for a loan application is made. The advantage of this approach is its flexibility in considering the results generated by challengers by using a wide range of alternative data (both transactional data and non-transactional data).
Running machine algorithms on borrowers’ data points can give the lenders the ability to make a variety of decisions and enhanced insights, like:
Like any other scoring system, alternative credit scoring methods have their own advantages and limitations.
ACS’ advantages and limitations coexist. The financial services industry, specifically the credit industry, has mixed opinions about the viability of alternative data. There are still many uncertainties regarding the future adoption of alternative data for use in credit scoring. A key question is how to best combine the use of alternative data with conventional financial data in order to improve credit scoring performance.
On one hand, it is believed that the adoption of alternative data will continue to rise with the explosion of digital interfaces or digital interface points with consumers. Another factor that is likely to bolster its adoption is the significant reduction of cost, computing power, and data storage.
On the other hand, there are concerns about its accuracy and dependability. As some of the possible alternative data points are subjective in nature, there are concerns as to whether one could go about measuring them.
Summary: A brief summary of the blog can be found in this presentation
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