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A Solution to Better Credit Scoring for the UnderBanked = Machine Learning + Mobile Banking

Nowadays, phones play an essential role in our lives. When not having identity cards with us, we show our virtual cards or fire up Facebook, LinkedIn, or online banking apps. It acts as a massive part of our lives. Surviving without mobile data refers to giving a paper without knowledge about the subject. In today’s techno-savvy world, mobile data is valid and serves as a necessity. 

Credit bureaus are critical components of any country’s financial infrastructure. They play an essential role in improving access to financial services, including credit transactions. Credit scoring is crucial for managing consumer credit risk, and mobile data usage has recently become popular. 

With mobile data-based credit scores, now an economical interface can help solve the problem of financial service for unbanked people. The new credit scoring method in terms of statistical and economic performance is a supporting variable to help the developing nations.

The Present Situation of Credit Scoring in the Financial Space

Image Source : Intellias

The concept of credit scoring through AI-first arose in the US and was initially deemed successful. To the extent that it was used as a primary, or even sole, mechanism for identifying one’s financial fitness and stability at a stretch. The new credit scoring (ACS) works with artificial intelligence and social media instead of paper-based scoring methods that depend on consumers having a bank account, turning the path towards e-commerce. It ultimately strives to be of higher financial inclusion. ACS refers to Alternative Credit Scoring. 

A study that used data from the World Bank’s Enterprise Surveys from 63 economies and over 75,000 firms discovered that establishing a credit bureau improves firms’ access to finance with longer-term loans, lower interest rates, and a higher share of working capital financed by banks. The greater the credit bureau’s coverage, the greater the scope and accessibility of credit information.

According to the World Bank, as of May 2019, 117 of the 191 economies studied had at least one credit bureau, covering between 1.2 percent and 100 percent of the working-age population in each economy. One-third of the total economies introduced their first credit bureau in the past decade, and more economies are in the process of establishing one in tune. Banks and other financial institutions of all 117 economies can access credit bureau data online through a website interface or system-to-system connection and media. 

Customers can request access to their credit data online in approximately 77 economies, read and understand their credit report online and dispute their credit data online. The largest credit bureau offers credit scoring and an online explanation of what these scores represent and how they are generally calculated.

Image Source : Intellias

Alternatives to credit score data sources are:

  1. Asset ownership
  2. Employment history
  3. POS and transaction data
  4. Data from energy services providers
  5. Self-reported bank data
  6. Telecom and mobile data  

Using mobile data for credit scoring:

  • Collect basic information of customer: full name, date of birth, address, phone number, history of payment, or entire bills. 
  • Capture mobile data payments: with the user’s permission, access mobile wallets data like spending and transaction data, information about top-ups, loyalty points, and card pickups.
  • Ask for additional information: Utility payments history, income information, remittances, savings, freelance, past evidence of lending, or credit history. 
  • Develop a proprietary scoring algorithm powered by machine learning: Machine learning is the best technological solution for risk scoring models. The alternative data by this algorithm boosts the predictive power of credit scoring models and provides a solution for all risk modules.
  • Call data records to understand the combination of datasets.
  • Customers’ debit and credit account information creates scorecards for credit card applicants while scoring.
    Machine learning methods for the easy solution of risk score are:

    • Classification
    • Logistics regression
    • Univariate analysis
    • Tree-based algorithms 
    • Support vector machines (SVM)
    • Scorecard creation
Image Source : Data Appeal

Extending value beyond lending: The first step towards long-term relationships with new or less acknowledged customers. Teaching consumers how to make better financial decisions using personal finance management tools. 

Consider provision for different savings accounts, insurance, micro-investing schemes, and other automated wealth management offers that appeal to lower-income account holders, making them strive for a good credit score.

Consumer financial literacy: Invest in improving customers’ financial literacy and creditworthiness to benefit from high loyalty and higher profitability at net worth. The rise of digital lending and, more specifically, alternative credit scoring in India has significantly boosted the economy. It provides a helpful framework for considering the social and ethical consequences of algorithmic decision-making. It broadens the scope and emphasizes the

trade-offs that governments and institutions must consider when balancing factors like privacy and fairness against access to credit and other social goods and interface.

The Bottomline 

In today’s time, modern technologies and advanced solutions allow more efficient processing of a large amount of information in a spin. It can play an instrumental role in expanding access to credit for the unbanked and set back people with thin credit histories. Globally, approximately 1.7 billion adults are still unbanked, meaning they do not have an account with a financial institution or a mobile money provider. Almost all unbanked adults live in developing countries, and women make up roughly 56% of all unbanked adults.

Credit bureaus have begun developing new ways to assess the creditworthiness of unbanked adults in developing countries using big data and machine learning. Today’s technologies can help transform massive information into insightful, real-time credit assessment for an improved credit score. It enables credit bureaus to reduce intake further, improve risk management, and increase access to credit at lower interest rates for the unbanked. The world can now be called as an empowering accelerated financial inclusion.

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