CredoLab: Revolutionising Credit Scoring Methods Through Mobile User Behaviour

Mobile phones have become ubiquitous across the globe. Not only that, but smartphones are in the hands of a large majority of users and leave an enormous digital footprint, with tens of thousands of data points that can be used to predict user behaviour. The sheer amount of Big Data is exactly why Credolab’s statistical models are so impressively predictive and stable.

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‍Each of these mobile phone accounts provides a particularly rich source of data: almost every detail about each call, text and information request is captured and stored by the mobile device and can be utilised. It’s not hard to see why many industries are looking to tap into such a valuable data source. The insights into customer behaviour are unparalleled, incredibly predictive and instantly available, and Credolab’s digital credit risk management is evolving rapidly as a result. 

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What Is Mobile Credit Scoring?

Mobile credit scoring assesses creditworthiness using data generated through smartphone interactions, including device and behavioural metadata. In some implementations, Mobile SDK credit scoring enables this data to be analysed directly within a mobile application and used alongside traditional bureau data. 

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Unlike telco data credit scoring, it does not rely on billing records, call activity, or network usage data owned by mobile network operators. Telco data is a separate alternative data source, although both approaches can complement each other within a broader credit decisioning model. 

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How Mobile and Behavioural Data Power Credit Scoring

Traditionally, credit reports and salary history provide the data that lenders require to make a risk assessment in developed markets. Credit bureaus utilize this limited data with few data points to establish a credit score. While this method has been reliably used in the past 50 years, it is simply inadequate in the present day since it cannot serve thin file customers, forming the majority of under and unbanked potential borrowers in emerging markets. Lenders want to establish three key factors: the customer’s identity, ability to repay, and willingness to repay. In emerging economies, however, these methods aren’t as effective. Today, there are over 2.5 billion people globally without formal financial services access, and lenders therefore have no access to previous borrowing behaviour.

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A lack of regular, fixed wages and formal savings adds to the financial inconsistency. Existing credit models do not serve the needs of economically active lower-income households and enterprises, which is why many lenders are seeking out an alternative approach. CredoLab’s easy-to-use, instant, and amazingly predictive alternative credit scoring solution aims at serving exactly these people.

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CredoLab’s significantly more inclusive risk models can be obtained using mobile phone data, with algorithms consistently developing highly predictive, stable statistical models and highly accurate scorecards for consumer lenders. The digital scorecard can be used as a standalone solution or alongside traditional bureau data, complementing rather than replacing traditional credit scoring applications. Device metadata and behavioural interactions can provide additional predictive power.

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Credolab allows lenders to gain greater control over their lending decisions, vastly expand their pool of qualified borrowers with little or no credit history, and reduce the overall risk. Lending has historically been, and remains, the bank’s main source of revenue. So digital credit scoring is sure to benefit the bank, whilst providing a more efficient and fairer service to the customer. The data collected from a customer’s mobile phone can help banks make better credit decisions and smarter loan evaluations.

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Despite being a promising resource, mobile phone data acquisition draws scepticism from customers with regards to security and authenticity. Data privacy is at the forefront of every customer’s mind, so how do credit scoring organisations reassure them that managing credit risk through digital tools is a friend rather than a foe?

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Telco Data vs Behavioural Device Metadata for Credit Decisions  

Using Telco data for credit decisions can be useful because it provides support through signals such as top-up frequency and value, call and mobile data usage, SIM age, and account tenure. Regular top-up patterns may indicate consistency in mobile spending, while changes in recharge behaviour can provide additional context around usage habits. 

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Call and data activity can help establish the continuity and intensity of mobile engagement. SIM age and account tenure can also provide useful signals about how long a customer has maintained the same mobile relationship. Together, these data points can help lenders understand aspects of a customer’s mobile usage history and add another layer of information to credit risk assessment.

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Credolab operates in a different, yet parallel, lane. With user consent, its software development kit (SDK) captures device and behavioural metadata based on how users interact with their smartphones. Credolab does not rely on telco network data. Instead, it analyses proprietary interaction metadata to generate behavioural intelligence that can strengthen credit risk assessment.

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The two data sources can complement each other within a lender’s risk stack. Telco data and device and behavioural metadata are sourced, governed, and accessed differently, but both can provide additional signals alongside traditional data. When used appropriately, they can help lenders build a broader and more informed view of credit risk.

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Smartphone-Based Credit Risk Scoring Accuracy

The accuracy of smartphone-based credit risk scoring is typically validated by backtesting scorecards against actual repayment outcomes. Lenders can compare predicted risk levels with observed customer performance using standard industry measures such as discriminatory power, stability, and default prediction accuracy. Performance generally improves as larger volumes of behavioural data become available, giving models more information from which to identify reliable patterns. 

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Where traditional bureau data is available, combining it with smartphone-derived devices and behavioural metadata can further strengthen predictive power. This hybrid approach allows behavioural risk scoring to directly complement established credit scoring rather than operate only as a standalone assessment method.

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How Does Credolab's Mobile SDK Work?

Credolab integrates through a lightweight software development kit (SDK) embedded into a lender’s or partner’s mobile app, or connected to its web onboarding flow. With user consent, it captures anonymised device and behavioural metadata without collecting personal or sensitive data. 

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This information is analysed to generate a real-time behavioural risk score, which is delivered through a unified application programming interface (API). Lenders can use the score as a standalone risk signal or combine it with traditional bureau or telco data to strengthen existing credit decisioning.

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If you want to expand your business, lower your risk and serve the huge under-banked and unbanked population, and are keen to learn more about how our solution can write to us at info@credolab.com or drop in a word here.

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Rules and regulations 

Regulatory requirements and privacy laws can stand in the way of organisations looking to gain access to digital data. Often, the data sets that lenders need are owned by, for example, telecoms companies, utilities, or retailers, which may themselves be prohibited from sharing information. Similarly, governments will be particularly cautious about sharing details about citizens.

‍Two solutions to these problems are to either pay for access, or to build partnerships with companies in a way which benefits both parties: on one side, there are organisations without financial services which can benefit from such arrangements and on the other, lenders can obtain consented access to valuable data. 

‍CredoLab collects data directly from the customer’s phone with their consent. What differentiates our product from all other solutions is that the collected data is completely anonymous metadata. This approach easily convinces customer to willingly share their data without worrying about potential exposure of personal and sensitive data, or any improbable but possible security breaches.

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Gaining credit insights

Converting metadata into credit insights poses a huge challenge to lenders. Risk and marketing teams will need to optimize their collaborations, and lenders will need to learn new skills to create evolved risk models. CredoLab’s know how, expertise and experience does that for lenders. Insights can be gained from the most unlikely sources: for example, the number of contacts, how much storage is utilized, and the time of day that phone calls are made – these “features” can be entered into models to determine credit scores. These emerging financial technologies are drawing the attention of a number of lenders, many of which are interested in CredoLab’s new algorithms, models and data sources.

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Once lenders have got hold of the necessary data, they have to know what to do with it. By its nature, non-traditional data is high in volume and often comes from disparate sources. For example, each mobile account can generate thousands of calls and texts per month, each with a diverse array of insight potential. Risk modellers therefore need to familiarise themselves with the new technologies enabling them to aggregate and analyse such metadata. If software isn’t up to date, huge volumes of data can overwhelm the system and make statistical analysis challenging. Recent developments, such as cloud computing, have improved the processing power available to lenders, bringing down costs as well as enabling actionable credit risk insights.

FAQs 

What is mobile SDK credit scoring?

Mobile SDK credit scoring uses software embedded in a lender’s app or web flow to analyse device and behavioural metadata, generating behavioural risk scores that support faster, informed credit decisions.

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How accurate is smartphone-based credit risk scoring?

Smartphone-based credit risk scoring accuracy is validated against actual repayment outcomes using standard industry performance measures. Predictive power generally strengthens with more data and when combined with traditional bureau information.

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How does telco data differ from mobile behavioural data for credit decisions?

Telco data reflects signals such as top-ups, call usage, data activity, and SIM tenure. Mobile behavioural data instead captures device metadata and behavioural interactions generated directly through smartphone use patterns.

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Is anonymised device metadata safe and compliant to use?

Anonymised device metadata for credit scoring can be used safely when collected with user consent, appropriate governance, and privacy controls. Credolab does not collect personal or sensitive data when generating behavioural risk scores.

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How can a lender start offering mobile credit scoring?

For lenders wondering, how to offer mobile credit scoring?, integrate a lightweight mobile SDK into its app or web onboarding flow, obtain user consent, and receive real-time behavioural risk scores through an API for credit decisioning.