How AI can tap into the unbanked using alternative credit scoring

alternative credit scoring for the unbanked, artificial intelligence, ai

Alternative credit scoring has been gaining global traction as it brings remarkable benefits for both consumers and lenders. It has the potential to increase financial inclusion and drive economic growth. Through artificial intelligence (AI), specifically machine learning, alternative data which comes from non-traditional sources such as financial transactions, web traffic, mobile devices, and public records can be used to assess the creditworthiness of the unbanked.

Let us first address who is the “unbanked” is before we jump right into explaining how AI can help the financial industry to tap into the unbanked. The unbanked are individuals without basic traditional financial services such as savings accounts, credit cards, or personal checks. According to Forbes, the unbanked have 3 basic needs electronic funds access, a debit card and the ability to quickly access cash. With traditional financial institutions unable to meet those needs, non-traditional banking services are stepping up to the challenge to provide reliable and affordable services.

How can alternative credit scoring help the unbanked?

Lenders and financial institutions use credit scores to determine a person’s or a business’s ability to get a loan. When it comes to the unbanked such as the young, poor, and small businesses, because of the lack of customer data or history to assess creditworthiness, getting a bank loan is out of the picture. Because of these barriers, many of them often turn to informal channels. For example, money order, check cashing, and payday loan companies despite the high-interest rates and risk of exploitation by unregulated players.

With alternative credit scoring, alternative data such as utility payments, rental payments, shopping history, mobile data, etc. will be used to better understand the repayment capacity of an individual or a business. Hence, helping lenders gain a complete view of an individual’s creditworthiness. This will allow the unbanked to have a higher chance of obtaining a loan.

How can AI help to assess alternative credit scoring?

AI is highly sought-after in the financial industry. Given its ability to work with a massive amount of data, we can see AI already being used by many financial organisations in areas such as banking, insurance, trading, and many others. Specifically for the unbanked, AI comes in to make finance more accessible, in addition to opening up greater opportunity for lenders to give out loans.

  1. Reduce processing time significantly. For alternative credit scoring, collecting alternative data points is just the start as the data needs to be analysed. Scanning through a massive amount of data can be tedious. What will take days will instantly turn to minutes, even seconds with AI. So, many modern lenders will choose to leverage AI technologies and machine learning to gain instant insights.
  2. Extract useful insights from unstructured data. Alternative data often is big, unstructured data coming from digital environments such as e-commerce and social media platforms. With the right machine learning model, AI can help identify data patterns that relate specifically to credit risk. This should help financial institutions make faster, more reliable decisions with deeper insight.
  3. Predict the ability to repay the loan. Lending requires a lot of data to make good business decisions. Since the value of the loan is tied to the creditworthiness of the individual or business that took out the loan, this makes it a business naturally suited for AI and machine learning. The more data you have about them, the better you can assess their creditworthiness. Traditional credit scoring systems make assumptions and test based on the customer’s credit payment history to predict their ability to repay the loan. However, a self-learning AI or machine learning analyses data, learns from it, improves itself, and provides predictions at a scale and depth of detail impossible for a traditional credit scoring model.

data, machine learning, wai hun see, artificial intelligence, alternative credit scoring

“Let the data tell you the story.” – See Wai Hun, co-founder & CEO of JurisTech

With only about 1bil out of 4.6bil people in Asia have access to formal credit, AI can make finance accessible. Mobile phones, e-wallets, and the sharing of financial information through open banking are consuming the world.  The use of AI and alternative credit scoring will bring the world one step closer to achieving financial inclusion.

If you carry the same vision to bring finance to the unbanked and increase financial inclusion, contact us to find out how you can use the power of AI to solve financial and social issues.

Juris Mindcraft: An AI that creates AI

Juris Mindcraft uses advanced machine learning techniques to learn from historical data and recognise patterns to build powerful predictive and prescriptive AI models. Taking into account non-traditional data sources. Juris Mindcraft has adapted its scoring model to target the unbanked. A great solution for alternative credit scoring to assess customer’s creditworthiness more accurately.

About JurisTech

JurisTech (Juris Technologies) is a leading Malaysian-based fintech company, specialising in enterprise-class software solutions for banks, financial institutions, and telecommunications companies in Malaysia, Southeast Asia, and beyond.

By | 2021-07-16T11:56:40+00:00 19th June, 2021|Artificial Intelligence, Insights|

About the Author:

Laura Tsen is the Senior Marketing and Communication Analyst of JurisTech. She does digital marketing that includes SEO and SEM. She is always intrigued by the world of technology and how it creates a future with endless possibilities. Having a passion to create and build awareness in embracing digital transformation to impact and improve the overall lifestyle of our society.