Why Alternative Data Is No Longer Optional

Imagine two borrowers. One submits a polished bank statement and a payslip from a desk job. The other has no formal credit history but pays every utility bill on time, runs a small business through a digital wallet, and tops up prepaid mobile credit with clockwork consistency. Traditional credit models would likely favour the first. But in today’s digital-first economy, the second borrower might be the safer bet — and this is where alternative data in lending is changing the game.

That’s the hidden value of alternative data: the real signals of creditworthiness that traditional models fail to see.

Most lenders still rely on static inputs like bureau scores, salary slips, and repayment history. These tell part of the story. But they miss what matters most in fragmented, fast-moving markets: financial intent, behavioural stability, and real-time income flows. In a world where many borrowers don’t fit the standard profile, these gaps are costly.

By the end of 2024, 62% of financial institutions globally had adopted alternative data to improve credit assessments. But most are only scratching the surface. They’re feeding powerful new data into outdated systems, without rethinking how decisions are made. This results in a growing disconnect between data potential and business impact.

Alternative data, from telco activity and e-wallet top-ups to psychometric scores and device metadata, holds insights that traditional credit models can’t access. Yet much of that insight remains untapped, trapped in silos or ignored by legacy infrastructure.

That’s why the market for alternative data in lending, already worth USD 18.7 billion in 2024, is projected to reach USD 135.8 billion by 2030. But adoption alone isn’t enough. What matters is how this data is used to unlock real value at scale.

This guide explores how lenders can do just that: turning hidden signals into smarter decisions, faster approvals, and more inclusive credit strategies.

But first, what exactly counts as “alternative data”, and why does it matter now more than ever? Let’s start by unpacking the fundamentals.

What Is Alternative Data?

Alternative data refers to any information used in credit decisioning that does not originate from traditional financial records or credit bureau files. It includes digital signals that reflect a borrower’s behaviour, financial habits, and lifestyle choices, often offering a more current, real-world picture of creditworthiness than legacy models.

These signals are particularly valuable in markets where many borrowers lack formal credit histories, consistent payslips, or long-standing bank accounts. For instance, behavioural indicators such as how consistently someone pays their utility bills, how frequently they top up their mobile data, or how stable their e-wallet balance is can all serve as proxies for financial responsibility.

Credible alternative data examples include:

• Patterns in airtime top-ups or data purchases that show spending regularity
  
• Prepaid electricity usage that mirrors income cycles
  
• E-wallet transaction histories that reveal earning and spending flows
  
• Rental records or education history that signal commitment and stability
  
• Psychometric assessments that reveal cognitive and behavioural risk traits
  
• Device metadata and usage consistency that reflect reliability

These are often leading indicators of risk, highlighting intent, discipline, and repayment likelihood before any defaults happen. In thin-file segments like gig workers, informal micro-entrepreneurs, or new-to-credit individuals, they’re essential.

With a clear sense of what alternative data includes, it’s important to contrast it with traditional credit data to understand where the gaps and the opportunities lie.

What Is the Difference Between Alternative Data and Traditional Data?

For example, a traditional credit report might show a one-time RM5,000 deposit from three months ago. An alternative data source — say, a digital wallet — might reveal ongoing RM200 top-ups every few days, painting a more accurate picture of income regularity and cash flow health.

This behavioural lens is transforming how creditworthiness is assessed. Specialised vendors now aggregate and standardise alternative datasets, making them usable by modern scoring engines. At the same time, regulators and central banks are beginning to incorporate these signals into formal frameworks. This clearly indicates that this shift is not only underway but accelerating.

Now that the difference is clear, the next question is: who’s actually putting alternative data to work in lending, and how?

Who Uses Alternative Data?

Lenders across the spectrum, from fintech startups to global banks, are using alternative data to strengthen credit decisioning. In markets with limited documentation, these signals offer faster, fairer, and more accurate risk insights. While alternative data has broader applications — from fraud detection in payments to insurance underwriting — lending remains its most active and impactful use case, powering everything from digital microloans to SME credit lines.

The way lenders use alternative data varies widely depending on their size, infrastructure, and target market. Here’s how different categories of financial institutions are operationalising these signals across lending segments.

Tier 1 Banks and Incumbents

Large banks are integrating alternative data through pilots and digital lending enhancements, layering it onto existing models to better assess thin-file and high-volume segments. Rather than replacing core systems, they use behavioural signals and open banking feeds to refine risk segmentation.

For instance, a major bank launching a digital loan product might blend telco data and cash flow patterns into its credit model. A borrower with limited credit history but consistent mobile and spending behaviour is instantly approved. No manual review needed.

Tier 2 and Tier 3 Banks

Regional and mid-sized banks use alternative data to improve credit access in underserved areas. With limited bureau coverage and outdated scoring tools, many partner with fintechs or integrate scoring engines to close information gaps.

Imagine a walk-in customer at a rural branch applying for a microloan without payslips. The bank’s system assesses telco payments, prepaid utility top-ups, and e-wallet usage, generating a real-time risk score and approving the loan on the spot.

SME-Focused Fintechs

Fintechs serving SMEs often bypass formal documentation, relying instead on real-time cash flow and supplier data to assess creditworthiness. POS activity, invoice payments, and utility bills form the core of their underwriting logic.

Take a corner shop owner seeking an expansion loan. The lender reviews six months of POS data, utility payments, and supplier invoices, identifying consistent revenue and financial discipline, then approving the loan immediately.

Digital-First and Mobile Lenders

Mobile-first lenders are built around alternative data. In credit-scarce markets, they rely on telco usage, device metadata, and psychometric scores to assess risk in real time, especially for gig workers and new-to-credit users.

A digital lender receives a late-night application from a gig worker with no formal income record. The platform scores their telco patterns, device consistency, and psychometric results, approving the loan and disbursing funds within minutes.

While adoption is growing, many lenders are still underestimating just how much value alternative data can unlock, especially when applied with precision across the credit lifecycle.

The Hidden Value of Alternative Data in Lending

Traditional credit scoring was designed for a different era: one where formal employment, stable incomes, and bureau visibility were the norm. But that model leaves millions of viable borrowers behind. Alternative data changes the game.

From increasing speed and precision to expanding access and enhancing strategic planning, these benefits underline why alternative data in lending is emerging as a core enabler of modern credit strategies.

Here are five ways alternative data is delivering real value, and helping lenders gain a measurable edge.

Broader Risk Visibility

Alternative data allows lenders to see creditworthiness where bureau-based models can’t.

This includes borrowers who have never taken formal credit but consistently demonstrate financially responsible behaviour, such as topping up prepaid electricity, transacting through e-wallets, or paying telco bills on time. These signals provide behavioural context that traditional models ignore.

By expanding the data lens, lenders reduce false negatives: borrowers who would’ve been rejected despite strong repayment potential, and improve segmentation by risk bands instead of just income tiers.

For instance, JPMorgan Chase has integrated alternative data into its credit assessment process to better evaluate thin-file and no-file borrowers. By incorporating payment histories from utility bills, rent, and digital transactions, JPMorganChase can identify creditworthy individuals who would traditionally be overlooked by standard credit bureau scoring models. This approach helps them expand financial inclusion while managing risk more effectively.

Faster Credit Decisions

Document-heavy, manual review processes delay credit decisions, and increase dropout rates. Alternative data enables real-time risk assessments based on digital signals that require no customer input or verification.

Telco data, device metadata, and app activity can be scored instantly, allowing embedded lenders and BNPL platforms to automate approvals within seconds — all while maintaining acceptable risk thresholds.

This speed is especially valuable in mobile-first environments where borrowers expect instant outcomes, and won’t wait for document checks or call-backs.

In South Africa, MTN, its API platform Chenosis, and TransUnion Africa recently launched the TransUnion Telco Data Score, a credit model that evaluates mobile calling behaviour in place of traditional credit history. This innovation enables near-instant loan approvals for millions previously excluded, while delivering a 25–35% gain in predictive accuracy over earlier models.

Higher Predictive Power with AI

When AI models are trained on behavioural signals, like app usage cycles, transaction rhythms, or shifts in location data, they can surface early risk indicators that conventional models miss. This unlocks sharper portfolio segmentation, earlier intervention, and more accurate pricing, especially in dynamic or thin-file segments where historical credit data is unreliable or unavailable.

Bank Rakyat Indonesia (BRI) has put this into practice by embedding alternative data, such as agricultural cycles and local market prices, into AI-driven credit scoring models. Through its digital lending apps, BRI delivers more precise risk assessments to rural borrowers with no formal credit history, enabling early risk detection and lowering default rates across underserved regions. 

By tailoring scoring logic to real-world behavioural patterns, BRI has improved both inclusion and performance, proving that AI and alternative data together outperform legacy approaches in frontier lending environments.

More Inclusive Credit Strategies

Credit inclusion doesn’t require lowering standards. Rather, it requires expanding the lens through which lenders view risk. Alternative data empowers institutions to build tailored scorecards for segments that are invisible to traditional models, such as informal workers, youth borrowers, or rural populations.

Instead of relying on credit histories or employer-verified income, lenders can use utility payments, mobile behaviour, and transaction consistency as proxies for credit intent and stability. The results in responsible access to credit, without loosening credit policy.

This has far-reaching implications in markets where financial exclusion is high and formal documentation is scarce.

In Brazil, Claro Brasil built a machine learning–based credit scoring model, Claro Score™, using prepaid mobile data, including top-up frequency, call activity, and payment behaviour, rather than relying on bureau reports or formal income statements. This alternative-data approach enabled the telco to identify low-risk users among prepaid subscribers, reducing first-payment default rates by approximately 31% and increasing conversion by 11% for postpaid upgrades 

Stronger Early-Stage Market Intelligence

Beyond individual credit decisions, alternative data provides a strategic advantage at the market level. By analysing behavioural signals in aggregate across geographies, demographics, or usage types, lenders can spot new lending opportunities and emerging risk concentrations long before they appear in performance data.

This is particularly useful for market expansion, product launches, or credit risk monitoring in previously untapped segments.

Instead of waiting for bureau trends or macro reports, lenders can proactively assess real-world borrower activity and adjust their strategy accordingly.

One example comes from Kabbage, a U.S.-based fintech company specialising in small business lending, which aggregated real-time cash-flow and transaction data across over 200,000 small businesses to create its Small Business Revenue Index. 

This index surfaced emerging revenue trends by sector and geography, enabling early visibility into rising or falling borrower activity at the market level. Rather than waiting for lagging bureau updates or economic reports, lenders could use this kind of alternative data to guide expansion into resilient segments or tighten exposure where distress signals emerged first.

Together, these five areas represent a massive, largely untapped reservoir of value. With the right models, tools, and data strategy, lenders can turn alternative data into a competitive advantage and build lending portfolios that are smarter, faster, and fundamentally more inclusive.

Yet, as with any powerful tool, alternative data comes with risks. As we will uncover in the next section, understanding these risks is critical to using it responsibly and sustainably.

What Are the Risks of Alternative Data?

The biggest risks of alternative data in lending stem from three core areas: regulatory uncertainty, lack of model explainability, and operational complexity. Without proper safeguards, lenders risk breaching compliance, introducing bias, or undermining customer trust. As the use of behavioural and real-time signals grows, so does the scrutiny around how they’re sourced, processed, and applied.

Understanding these challenges and addressing them with governance, transparency, and scalable systems, is critical to sustainable adoption.

Regulatory Uncertainty

One of the biggest risks facing lenders is the lack of clear and consistent regulatory guidance around alternative data.

Many use cases, such as credit scoring based on mobile metadata or psychometric inputs, fall into legal grey areas. What’s permitted in one jurisdiction may raise compliance flags in another. For global lenders or cross-border deployments, this creates ongoing complexity.

One of the biggest risks with alternative data in lending is regulatory ambiguity. What’s allowed in one market may raise red flags in another, especially with emerging use cases like mobile metadata or psychometric scoring. 

For instance, GDPR in the EU mandates strict consent and explainability, while the FCRA in the US leaves room for broad interpretation. In Southeast Asia, innovation is outpacing oversight, and across Africa, regulatory stances range from progressive to prohibitive. For cross-border lenders, navigating this patchwork is anything but straightforward.

How to manage it:

• Engage early with regulators or local industry associations to track evolving requirements
• Choose decisioning systems with configurable rule engines, allowing for policy tailoring by jurisdiction
• Maintain audit-ready documentation on data sources, consent flows, and decision logic
  

Lack of Model Explainability

Alternative data often relies on unstructured inputs and advanced AI, which can make models hard to explain and audit. Without safeguards, these models can introduce hidden bias or unfair outcomes.

Borrowers may be penalised based on signals that don’t meaningfully correlate with credit risk, such as device type, time-of-day app usage, or browsing patterns — triggering concerns around fairness and transparency.

Regulators are increasingly demanding explainability and consumer clarity, especially where AI/ML is used in credit decisions.

Consent and control are equally critical. Borrowers have the right to know what data is being collected, how it’s used, and whether they can opt out, particularly under privacy regimes like GDPR and CCPA.

How to manage it:

• Adopt explainable AI frameworks (e.g. LIME, SHAP) or interpretable models for high-impact decisions
• Monitor model fairness metrics, such as adverse impact ratios, across demographic groups
• Implement consent orchestration tools that give borrowers clear visibility and control over data sharing
  

Striking the right balance between innovation and responsible governance is essential, not just to reduce regulatory exposure, but to maintain customer trust and brand integrity.

Operational Complexity

Beyond compliance and fairness, alternative data also introduces practical risks tied to how systems are built, vendors are selected, and teams are aligned internally.

Key risks include:

• Data provenance and traceability
  Without clear lineage and audit trails, decisions made using external or unstructured data can be hard to justify. This raises issues with regulators, auditors, and internal risk committees.
  
• Vendor lock-in and limited flexibility
  Some early platforms have rigid architectures, limited explainability, or black-box scoring logic. This makes future scaling, model changes, or audits painful.
  
• Siloed implementation
  When risk, compliance, IT, and product teams are not aligned from the outset, adoption slows, internal buy-in weakens, and long-term scalability suffers.
  
• Reputational fallout
  Poor handling of sensitive data or unexplained declines can spark backlash, especially in markets with rising consumer awareness around digital privacy.
  

How to manage it:

• Select vendors with open architecture and full traceability for all scoring inputs and decisions
  
• Bring cross-functional stakeholders into the planning process early, including legal, IT, risk, and compliance
  
• Pilot before scaling, with clearly defined KPIs, to identify issues before they escalate
  

For decision-makers, the challenge isn’t simply to adopt alternative data, it’s to do so in a way that’s modular, explainable, auditable, and scalable across the lending lifecycle.

When managed well, the risks become manageable, and the upside becomes transformative.

Mitigating these risks isn’t just about policy, but execution. That’s why operationalising alternative data effectively is the next frontier for forward-thinking lenders.

How to Operationalise Alternative Data in Lending

Adopting alternative data is one thing. Turning it into production-ready credit decisions is something else entirely.

Lenders often underestimate the gap between collecting new data and applying it at scale — responsibly, accurately, and in line with regulatory expectations. Simply plugging fresh signals into a legacy scorecard won’t work. You need a structured lending stack: one that can ingest diverse data, train models, execute decisions in real time, and evolve with your business.

JurisTech’s AI-enabled solutions help financial institutions operationalise alternative data across the full credit lifecycle.

• Juris MindCraft is a predictive AI platform for credit risk that supports data ingestion, feature engineering, ML model training, and policy-aligned scoring — all with built-in explainability.
• Juris DecisionCraft is a business rule engine that automates real-time approvals and policy execution by interpreting model outputs, enforcing credit policies, and managing escalation flows.
  

Here’s a four-step framework to guide lenders from data to scalable, compliant decisioning.

Step 1: Score Borrowers Using ML Trained on Alternative Data

The first step is converting raw, semi-structured signals, such as telco usage, e-wallet activity, and utility payments, into reliable credit scores. This requires machine learning pipelines that can handle messy, behavioural data and distill it into patterns that correlate with risk. These pipelines must seamlessly ingest varied sources, engineer features that reflect behavioural risk, and support robust model training, validation, and retraining workflows.

Many institutions use predictive AI platforms built for credit risk to automate this process at scale. Juris MindCraft enables lenders to deploy such models with built-in explainability and governance controls, ensuring each score is transparent, auditable, and defensible.

Step 2: Align Models to Credit Policies with Explainable AI

Once a model is trained, it needs to be aligned with institutional credit policy. That means defining approval thresholds, risk tolerances, pricing logic, and fairness safeguards, and making sure they’re consistently applied across every decision. AutoML platforms that offer policy-alignment features allow risk teams to configure approval criteria by customer segment or product, apply fairness thresholds, and embed explainability at the model level.

This step ensures predictive models don’t drift from business objectives or regulatory boundaries. Juris MindCraft’s AutoML engine enables credit teams to manage this alignment directly, maintaining full auditability while adapting to evolving risk and compliance needs.

Step 3: Automate Approvals with Real-Time Decisioning Engines

Model outputs are only valuable if they’re acted on instantly, reliably, and in accordance with policy. Real-time decisioning engines close the loop by automating approvals, declines, and referrals based on live borrower data. These systems interpret ML scores, apply policy logic, and handle override scenarios or escalations, all while maintaining processing speed and consistency.

This is especially critical in digital lending and SME underwriting, where time-to-cash and rule compliance drive both customer experience and performance. Juris DecisionCraft enables lenders to embed model-driven, policy-compliant decisioning across every credit product, with traceability built in.

Step 4: Choose a Platform That Can Operationalise at Scale

Before scaling, it’s essential to ensure your infrastructure can support end-to-end operations, and not just isolated pilots. Many lenders struggle to transition from experimental scoring to enterprise-grade deployment due to platform limitations. A future-ready system must integrate both structured and unstructured data, maintain traceability across all models and decisions, and operate in real time, whether serving a mobile-first consumer or embedded SME lending product.

Equally important is an open, modular architecture that avoids vendor lock-in and can adapt to regulatory change. Juris DecisionCraft is built for precisely this kind of scale, supporting alternative data lending strategies across Southeast Asia and beyond.

Make Alternative Data Work, With a Partner Who Knows How

Operationalising alternative data is not a side project. It’s a strategic capability. From raw behavioural signals to final lending decisions, each layer of the stack must be connected, explainable, and built to scale.

When done right, alternative data in lending doesn’t just improve credit scoring. It changes how lending works: faster, fairer, and more inclusive.

But real success demands more than data pipelines and machine learning. It requires clear policy alignment, governance readiness, and enterprise-wide orchestration.

That’s where the right partner matters.

At JurisTech, we help lenders move from experimentation to execution, with modular AI-driven tools and hands-on advisory designed for real-world conditions. Whether you’re modernising existing scorecards or launching new digital products, we work with you to embed alternative data into decisioning that’s scalable, transparent, and fully aligned with your goals.

Turn hidden signals into real lending advantage today. Book a free consultation with our team to explore how we can help you get there.

About JurisTech

JurisTech is a cloud-native, global-leading company specialising in enterprise-class lending and recovery software solutions for banks, financial institutions, telecommunications, and automobile companies worldwide. We embrace a microservices architecture to ensure scalability and flexibility in our solutions.

We power economies by reimagining financial services with cutting-edge software solutions, leveraging composable architecture and generative AI. Our offerings include artificial intelligence (AI), auto-decisioning, digital customer onboarding, loan origination, credit scoring, loan documentation, litigation, and debt collection. Our solutions have enabled businesses across a broad array of industries to undergo digital transformation, providing enhanced customer experiences and, most importantly, achieving their business goals.

JurisTech has been mentioned as a Representative Provider for Lending Ecosystems, as a Representative Vendor for Commercial Loan Origination Solutions, and as a Sample Vendor for Commercial Banking Onboarding across Gartner reports in 2024.