What’s New with Alternative Data and How It’s Revolutionizing Risk Assessment

In today's digital age, risk scoring is a vital aspect of assessing an individual's creditworthiness. However, traditional credit scoring models have certain limitations, such as not accounting for new-to-credit consumers, being unable to verify identities, and not detecting fraudulent activity effectively.

With the rise of alternative data, an opportunity exists to overcome these limitations and create a more accurate credit risk assessment, especially for consumers with very thin or no credit files.

Alternative data refers to non-traditional sources of information that can be used to enrich user profiles and establish a more comprehensive digital footprint. These sources of data include online behavior, social media activity, phone number/email address data, and other data that can be used to assess an individual's creditworthiness. Get ready to discover the latest game-changer in risk scoring!

Let’s explore how alternative data is changing the credit risk scoring landscape and its potential benefits, like helping solve the global financial exclusion issue.

What are the Advantages of Alternative Data?

One of the most significant advantages of alternative data is that it can enrich identity data, especially for new-to-credit consumers.

This fact is a game-changer, globally. According to the World Bank, around 1.4 billion adults worldwide and 230 million people in India do not have access to formal financial services, such as bank accounts, credit, or insurance.

A report by the Consumer Financial Protection Bureau (CFPB) found around 45 million Americans have limited or no credit history, making it challenging for traditional credit scoring models to evaluate their creditworthiness. Financial exclusion can make it difficult for people to improve their lives. Saving money, investing in businesses, and accessing credit is hard for those excluded from the financial system and can limit economic growth and exacerbate poverty.

Alternative data can help address this issue by providing additional information about an individual's financial behavior.

For instance, payment history for utility bills, mobile phone bills, rent payments, and other subscription services can help determine creditworthiness. This additional data helps paint a more complete picture of an individual's creditworthiness, leading to better credit risk assessments.

How Does Alternative Data Detect Fraud?

Another significant advantage of alternative data is that it can help detect fraudulent patterns. With the increasing prevalence of digital fraud, credit scoring models need to adapt to prevent fraudulent activity before it occurs.

Alternative data can play a critical role in this process by flagging suspicious and inaccurate information in credit applications, also known as application fraud. For example, alternative data can be used to detect synthetic data by conducting simple PAN, SSN and DOB checks. If the data returns a non-match or “not found” result, more than likely that identity is synthetic.

Additionally, analyzing social media behavior and online activity can provide insight into fraudulent activities. Other ways that alternative data can be used to detect and prevent identity-based fraud include:

Identity Verification: Alternative data can be used to verify an applicant's identity, such as biometric data. This can help ensure that the applicant is who they claim to be and prevent identity theft.
Credit History: Credit history is one of the most important factors in assessing creditworthiness. Alternate data can be used to assess an applicant's credit history, particularly for those with little or no credit history.
Behavioral Analysis: Alternative data can be used to analyze an applicant's behavior, such as social media activity, e-commerce data, and digital footprint. This can help detect patterns of fraud or suspicious activity.
Employment Verification: Alternative data can be used to verify an applicant's employment history, income, and other financial information. This can help detect fraudulent applications that contain false or misleading information.
Machine Learning: Machine learning algorithms can be trained on large datasets of historical application data to identify patterns of fraud and predict the likelihood of fraudulent applications. This can help financial institutions detect and prevent fraud quickly and accurately.

Fraud is a perennial problem. A study by the Association of Certified Fraud Examiners (ACFE) found that fraud cases involving identity theft had increased by 30% in the past year, highlighting the importance of detecting and preventing fraudulent activity. Alternative data is a solution to reduce fraud risks.

Why is Alternative Data a Risk Scoring Game-changer?

Alternative data can offer several benefits to credit risk assessment, including creating a unified customer identity and establishing a more comprehensive digital footprint.

Create Unified Customer Identity: Alternative data can combine an individual's KYC (Know Your Customer) information, personal information, device, and behavior intelligence to create fraud-proof identities. This can lead to a more accurate credit risk assessment, as the combined data offers a more comprehensive view of the individual's financial behavior.
Establish Digital Footprint: Alternative data can establish a user's risk profile by extracting valuable demographic, behavioral, and identity attributes. For example, an individual's social media activity can provide insight into their interests, lifestyle, and financial behavior. Similarly, mobile device data can provide information about their location, usage patterns, and purchasing behavior.
Phone Number/Email Insights: Alternative data can also provide insights into an individual's phone number and email address. For example, analyzing an email address can provide information about the user's profession, education level, and financial behavior. Similarly, analyzing a phone number can provide insights into their location, device usage, and communication patterns.

How do Alternative Data Templates Work?

One of the significant advantages of working with Bureau for alternative data is that it offers ready-to-use templates that can be easily integrated into existing workflows. These templates can leverage alternative data and customize them to suit the specific business needs of a company. The availability of these templates is particularly beneficial for businesses that are new to using alternative data in their risk-scoring models.

According to a report by Experian, financial institutions that use alternative data to supplement their traditional credit data have seen up to a 20% reduction in default rates. By incorporating templates for alternative data, businesses can save time and resources while obtaining valuable insights to improve their risk assessment models. These templates can provide a significant competitive advantage for businesses, enabling them to leverage the power of alternative data with minimal effort.

How Does Alternative Data Help Fintech and Financial Institutions Make Intelligent Lending Decisions?

Alternative data plays a significant role in the future of credit risk scoring. By providing a more comprehensive view of a customer's behavior, alternative data can help banks, fintech, and other businesses make more informed decisions about the trustworthiness of an identity. Ultimately, the knock-on effect is a significant reduction in costly defaults. In turn, this can increase access to credit for millions of people who the traditional credit scoring system has historically underserved.

Bureau has helped multiple banks and fintech companies create a risk profile of users by using alternative data behind the consumer’s phone number and email along with social, financial and digital presence.

Project Hero: A case study

For example, a leading quick loans provider in India uses the digital age of phone numbers and email and depth of digital presence to gauge the plausibility of instantaneously disbursing quick loans.

The impact of alternative data is not limited to financial entities. Bureau is working alongside Project Hero to drive digital inclusion for the underserved and traditional unregulated construction industry.

Project Hero provides a platform for construction workers and contractors to connect, engage in gigs, build relationships and reputation, and avail financial services. By using Bureau’s alternative data, Project Hero is gauging reachability, roaming status, and history associated with the phone numbers of construction gig workers, thus instilling more trust in the ecosystem.

However, for alternative data to become an integral part of the credit-scoring and risk-profiling ecosystem, it is crucial to ensure complete trust in the data. To achieve this, businesses must ensure that they are collecting data ethically, with consent and permission from consumers. They must also be transparent about how they use this data and ensure that the data is accurate and up-to-date.

Fortunately, regulatory bodies are already taking steps to ensure the responsible use of alternative data in risk scoring. In the United States, the Consumer Financial Protection Bureau (CFPB) has published guidelines for the ethical use of alternative data in credit decisions. The guidelines encourage lenders to use alternative data in a manner that is transparent, explainable, and non-discriminatory. In India you have the Data Personal Data Protection Act.

Alternative data presents significant opportunities for businesses to improve their risk assessment models and provide access to credit to underserved populations. By leveraging alternative data, businesses can create more comprehensive customer profiles, detect fraud patterns, and establish digital footprints. Additionally, businesses can save time and resources by using Bureau’s templates for alternative data while obtaining valuable insights. However, to ensure complete trust in alternative data, businesses must use this data ethically and transparently, while ensuring that it is accurate and up-to-date. Set up time to learn more.

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