Credit Data Integration: A Step-by-Step Beginner's Guide

Traditional credit scoring pain points‍

Although the criteria used in the different credit reports could vary according to each institution, all traditional credit studies determine the chances of someone getting a loan through credit history. Credit history is a record that gathers all the financial data of a person, such as:

‍

• Payment history: If payments are made on time, payment frequency, and when these payments are due.

• Credit utilisation: How much a person owes on loans and credit cards.

• Length of credit history: The longer the person's payment history, the higher the score.

• Combination of existing credits and account types: If a person has a combination of accounts, including instalment loans, home loans, credit and retail cards.

• Recent Credit Activity: If a person recently opened many new accounts, it could suggest potential financial problems and lower the score.

‍

However, one of the biggest problems with credit history is that it is limited to the data within the banking system. Thus, millions of people who are unbanked but are trustworthy (such as Gen Z, millennials, immigrants, or informal workers) are most likely to get a bad score, losing the possibility of acquiring a secure loan. Therefore, some finally apply for informal credit, getting into a debt trap.

‍

In addition, traditional credit scoring assessments are manual and tedious and do not have the technology necessary to include behavioural data. In this way, they cannot qualify millions of clients simultaneously or expand their offer outside the financial system without incurring high risks.

‍

Therefore, alternative credit scoring arises from the need to serve thin-file customers, forming the majority of underbanked and unbanked potential borrowers, especially in emerging markets. Now, lenders could boost financial inclusion and grow by tapping new, unexplored segments!

‍

A New Way to Assess People With Alternative Data‍

Alternative credit scoring beats traditional credit methodologies by using machine learning algorithms and new alternative data sources related to people’s behaviours, such as:

‍

• Rental payment and subscriptions
• Emails
• Social networks behaviours
• Financial reports
• Geolocations

‍

The growth of alternative data is exponential, powered by the fact that more and more users are storing information on the network, generating new data daily. In this way, in contrast to traditional scoring, alternative scoring has the capacity to anticipate movements and obtain more accurate patterns of an applicant’s willingness to pay.

‍

These innovative data enrichment systems can complete a person’s profile in real-time, allowing companies to understand their prospects better and make substantially more informed decisions. In fact, including these thin-file customers represents an interesting growth opportunity for many businesses. For instance, millennials (despite their lack of financial profiles) represent the largest adult cohort in the world, making up 23% of the global population.

‍

Alternative data also improves a company’s self-knowledge. A large amount of existing alternative data allows institutions to perceive their performance more accurately. The connection of these insights enables companies to correctly interpret how they are perceived by people, comparing themselves against competitors, suppliers, and other secure connection networks.

‍

‍

Step-by-Step: How to Integrate Alternative Credit Data

Adding alternative credit data to your risk model is simple when done step by step. Testing it on smaller groups first helps you make smarter lending decisions easily.

‍

Here's how you can seamlessly put this strategy into action:

‍

• Step 1: Define the use case and target segment
  The first step to credit data integration is to identify the credit decision the data should support and the customer group involved, such as thin-file borrowers, near-prime customers, or SMEs.

• Step 2: Choose relevant data sources and providers
  Select the data source for integration that fits the use case, including bureau-adjacent information, cash-flow data, or device and behavioural metadata.

• Step 3: Integrate through an API or SDK
  Connect the selected data source through an API or SDK and map its fields to the relevant attributes in the existing risk model. Credolab provides Android, iOS, and web SDKs as an example of a low-friction integration approach.

• Step 4: Validate before deployment
  Test the approach using historical or pilot data and compare its predictive lift with the existing model before applying it to live credit decisions.

• Step 5: Add it to existing scorecards
  Use alternative credit data as a secondary signal, score overlay, or segmentation layer. This allows lenders to strengthen existing models without replacing them wholesale.

• Step 6: Monitor performance and compliance
  Track changes in approval rates, default rates, and model drift after deployment. Continue reviewing compliance requirements to ensure the scoring approach remains appropriate as data, models and regulations evolve.

‍

​

Alternative scoring challenges: How to integrate multiple data

Data sources are increasingly diverse today, so businesses must be able to nail the ‘how to integrate data from multiple sources’ aspect. They need to organise them and identify the ones that are most relevant before going forth with credit data integration. Integrating multiple credit scores in a single model can be difficult, but the predictive power increases once more scores are ingested into the model. Nevertheless, this is only true if the scores (and the underlying data sources) have little correlation with each other.

‍

Another important aspect of credit data integration to consider is the legal angle of information. Personal data collection and use are regulated differently in each region. It is imperative to understand the legal terms that govern the storage of information, where the information originates, the implementation limitations, and how it should be used correctly and ethically.

‍

Credolab complies with all existing regulations because the data collected is consented to by clients and is in the form of anonymous metadata without any Personally Identifiable Information (PII) ever leaving the device.

‍

A Deep Dive Into Behavioural Data

New scoring models based on machine learning provide behavioural insight, providing a holistic view of human beings. “Some obvious behavioural data sources include psychometric data, device data and web browsing data. Some companies that use behavioural data (psychometric and non-traditional applicant data) are Aire, EFL and ConfirmU. ConfirmU, for example, has experts from the fields of psychology, psychometrics, technology, data science, gamification and risk analytics, all contributing to creating a highly predictive tool for credit risk.‍

‍

Credolab only collects unique user behavioural data once from each borrower. Therefore, each user’s behavioural data is unique and does not change significantly over time. There are more than 10 million features created for each applicant, such as:

‍

• Weekly events created,
• Apps installed in a week,
• Registered accounts,
• Updated apps

‍

Behavioural and device metadata is often integrated after bureau and cash-flow data, giving lenders an additional layer of insight once core financial information is in place. Since this data can require SDK embedding, introducing it later can simplify the ‘how to integrate nontraditional data for credit approvals’ quotient and allow lenders to build on their existing credit infrastructure.

‍

What attributes are most predictive?

Credolab tailor-makes each scorecard for each client. As such, and given the enormous amount of behavioural features that Credolab calculates, it is difficult to pinpoint the individual attributes that are always predictive, as not one client is ever the same. Here are a few examples of the macro-categories of data Credolab collects and processes that contribute to the likelihood of loan payment:

‍

• Calendar – A higher number of events scheduled during the working hours of working days may show a higher willingness to pay.

• Contacts – A lower number of contacts with more than one phone number may tend to indicate a lower willingness to pay.

• Application – A higher number of gambling apps installed may indicate the customer is associated with high-risk behaviour and more likely to default.

‍

Behavioural analysis benefits

By combining digital footprint lookups with behavioural data, lenders can obtain at least four advantages over the competition:

‍

• More pleasant customer journey: Beyond what is mandated by law, only those customers found to be less trustworthy based on their risk scores will need to be asked for additional documentation and/or information at underwriting, with different levels of hard or soft checks per customer – according to lender preferences.

• Proactive assessment for faster results: This also allows for pre-KYC monitoring and first assessment. By investigating mobile and web metadata, emails, and phone numbers at sign-up, more obvious attempts at fraud are blocked, freeing up valuable resources and making systems and processes safer.

• Optimised workload management: Moreover, any potentially problematic applicants (those with middle-of-the-road initial risk ratings) can also be flagged to give the team a better prediction and understanding of the affordability and underwriting workload ahead.

• Privacy-first appreciated by customers: Because customers are only asked for the minimum of what is required for their particular case, privacy concerns will be addressed, with the privacy-minded not experiencing intrusion or insult. Meanwhile, the data collected is compliant with privacy legislation. It is public, open data helping to gauge patterns of behaviour that identify customers’ legitimacy and truthfulness – not their private information.

‍

Common Integration Mistakes to Avoid

• Choosing data without a clear use case: Select alternative data based on the specific customer segment and credit decision it needs to support.

• Treating alternative data as a replacement: Use alternative signals to complement traditional data rather than replacing established scorecards without validation.

• Skipping data quality checks: Assess coverage, consistency, and reliability before introducing a new data source into the risk model.

• Integrating without proper validation: Test predictive power on historical or pilot data before using alternative credit scoring for live decisions.

• Overlooking integration requirements: Confirm API or SDK requirements, data mapping, and system compatibility before implementation begins.

• Ignoring compliance requirements: Review privacy, consent, data usage, and applicable regulatory requirements throughout the integration process.

• Failing to monitor model performance: Track approval rates, default rates, and model drift after deployment to identify changes in performance early.

‍

Alternative scoring challenges: How to integrate multiple data

FAQs

How long does it take to integrate alternative credit scoring?

Integration of alternative credit scoring typically takes one to four weeks. Standard API or SDK deployments can be completed in a few days, while customised architectures can take more time.

‍

Does alternative data integration require replacing our existing credit model?

No. Alternative data can work alongside existing models as a secondary signal, score overlay, or segmentation layer.

‍

What is the difference between an API and an SDK integration for alternative data?

An API connects systems to exchange data or scores. An SDK is embedded into an application or website to enable data collection or processing within that environment.

‍

Is alternative data integration compliant with lending regulations?

It can be, provided data collection and use meet applicable privacy, consent, consumer protection, and lending requirements. Each data source should be reviewed before deployment.