Future Trends in Alternative Credit Scoring for Fintech

As the world goes digital, the financial sector is changing how it evaluates creditworthiness. 

Traditional scoring models, which depend on financial history, leave out around 1.4 billion unbanked people worldwide, according to the World Bank. 

This is where alternative credit scoring comes in, with fintech innovators leading the way. 

By using alternative data for credit scoring, this approach creates a fuller, more accurate view of applicants. This information is harder to fake and it better reflects a person’s true credit potential.

In this article, we’ll look at the latest trends in alternative credit scoring for fintech, with a focus on emerging markets where banking access is limited.

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6 key trends in alternative credit scoring 

To stay ahead in the market, lenders must stay updated on the latest alternative credit scoring trends.

Here are the main ones:

1. Increased adoption of artificial intelligence (AI)

2. Increased integration of alternative data

3. Focus on promoting financial inclusion

4. Modifications in regulations and compliance requirements

5. Decentralized scoring using blockchain

6. Partnerships between fintech, traditional financial institutions, and tech companies

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#1. Increased adoption of artificial intelligence (AI)

The global AI market in fintech is estimated to be worth $14.2bn in 2024. 

Experts predict it will grow at an average annual rate of over 20% and increase fivefold by 2033.

Here are the key changes fintech companies anticipate with the adoption of AI.

Ethical AI and transparency

Currently, the use of AI and machine learning (ML) is somewhat limited by regulatory requirements.

This is because of the “black box” nature of AI models, while regulations require clear standards for transparency and explainability.

As a result, lenders will be encouraged to adopt explainable AI, also known as “white box” artificial intelligence, as an alternative to “black box” models.

Application of predictive modeling

This process relies on the use of advanced algorithms to estimate the likelihood of loan repayment. 

Predictive credit score modeling uses a variety of alternative data processed through innovative technologies like regression analysis and neural networks.

By analyzing various factors, lenders can assess the potential risks of lending to an individual.

Real-time credit decisions

AI is ideally suited for processing huge amounts of data in real-time. 

Machine learning models assess risk by identifying patterns and anomalies in borrower behavior. This allows lenders to make instant lending decisions without manual intervention.

The use of AI in credit risk management enables online detection of suspicious activity and potential fraud. 

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#2. Advanced integration of alternative data

Shortly, fintech companies will increasingly rely on alternative credit scoring, an approach to assessing the creditworthiness of potential borrowers that relies on non-traditional data sources.

Here are examples of alternative data that can be used:

• Social media activity.
• Data from online resources like e-commerce, streaming, and delivery services.
• Email address information (creation date, associated accounts).
• Phone number data (operator, registered accounts, device type).
• IP address data (geolocation, regional risk assessment).

All these alternative credit data form a digital footprint of potential borrowers. The data from its analysis will be increasingly taken into account by lenders in alternative credit scoring models. 

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#3. Focus on promoting financial inclusion

Limited financial inclusion is a serious problem. It is especially acute in developing countries. 

In Nigeria, only 40 percent of people have recourse to banking services. In Mexico, the situation is even more striking, with only 37% of the working-age population holding bank accounts.

The situation in these and some other countries is shown in the table below. 

The formation of alternative credit scores allows lenders to find a way out of this situation and increase financial inclusion. Unlike traditional banking services, the Internet has a global reach, making it accessible to more people.

For example, let's examine the statistics on internet users in Nigeria and Mexico to highlight this point.

According to Statista, almost 75% of Mexicans are active online, and their number will continue to increase:

In Nigeria, the figure is slightly lower at around 45%. However, it is still higher than the percentage of residents covered by banking services.

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#4. Modifications in regulations and compliance requirements

Regulatory requirements for assessing applicants’ creditworthiness are continuously evolving, and financial institutions need to regularly review and update their processes.

In the near future, innovations may impact the following areas:

• Use of alternative credit scoring models
• Protection of user data privacy
• Ensuring consumer protection

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#5. Decentralized scoring using blockchain

Decentralized scoring enables the use of blockchain technology to create a reliable credit score.

Here are some of the benefits of this approach:

1. Transparency of alternative credit scoring. Blockchain offers a permanent record of financial transactions that can be verified by all parties, eliminating the need for centralized authorities.

2. Data ownership. Borrowers retain control over their information. This means they can decide what data to share. 

3. Accessibility of lending. Decentralized scoring uses a wide range of non-traditional data. This gives unbanked populations access to the services of fintech companies.

4. Enhanced security. Blockchain technology protects sensitive financial data from unauthorized access by fraudsters.

5. Budget savings. The absence of the need for intermediaries reduces the cost of assessing the creditworthiness of applicants.

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#6. Partnerships between fintech, traditional financial institutions, and tech companies

Interaction between fintech players, traditional banks, and software development companies can revolutionize credit scoring.

Each has a crucial part to play in this process.

• Fintech companies offer new technologies and solutions to assess creditworthiness – for example, analyzing the digital footprint of borrowers.

• Traditional financial organizations such as banks and credit unions have expertise and access to borrower data. 

• Technology companies can process vast amounts of information and make predictions.

When they come together, fintech companies bring innovation, banks bring financial expertise and market access, and IT companies provide the technical infrastructure. 

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RiskSeal driving change

Credit scoring platform RiskSeal is leading the way in alternative credit scoring. We target low-income and high-risk demographics around the world.

Working with us can bring several benefits to the lender:

1. Financial inclusion. Approve more loans among unbanked populations. 

2. Enhanced accuracy. Consider a wider range of factors when assessing credit risk.

3. Streamlined decision-making. Process more loan applications without compromising accuracy. 

4. Adaptability to changing circumstances. Machine learning algorithms continuously learn and change in response to changing market conditions and consumer behavior. 

5. Risk mitigation. Recognition of patterns and trends that traditional models might overlook. 

6. Operational efficiency. Reduce manual labor involvement by reducing errors and speeding up the entire loan decision process.