Bridging the Gap: Recalibrating Credit Models to Serve Pakistan’s SMEs and Consumers

In Pakistan, a country of over 220 million people, access to formal credit remains a persistent hurdle. Small businesses lack the capital to grow, consumers struggle to finance essential life goals, and even those with verifiable income often find themselves overlooked by traditional credit frameworks. This isn’t for lack of interest: Banks and fintechs actively seek to expand their reach. Yet the barriers remain stubbornly high, with outmoded scoring systems—built on sparse or irrelevant data—leaving millions of capable borrowers behind. Imagine Usman, who runs a thriving neighborhood grocery store in Lahore. Despite healthy daily sales, he struggles to obtain a growth loan because the bank wants more than just his cash flow evidence—it wants collateral or a polished credit history. Usman’s robust day-to-day transactions never make it into the credit assessment process. Zainab, fresh out of university with a promising business plan, hits a wall when trying to secure a modest line of credit. Lacking substantial collateral or a formal employment record, she’s instantly categorized as high-risk. No matter her academic background or future potential, the rigid old-school models can’t see beyond the missing paperwork. Financial institutions, saddled with limited tools, continue to underwrite based on outdated metrics. Even those that embrace new technologies often turn to “alternative data” (like mobile or telco usage), which can fail to accurately distinguish low-, middle-, or high-income users due to a lack of meaningful variance. These stories illustrate a common theme: Traditional credit assessments rarely capture the realities of Pakistan’s economic vibrancy. In many emerging markets, the buzz around using non-traditional data sources—such as mobile wallets, social media profiles, or utility payments—for credit scoring has been loud. But while these methods have garnered global attention, they haven’t always translated into reliable results on the ground. Here’s why: Whether it’s monthly mobile usage or a handful of digital wallet transactions, alternative data often doesn’t reflect the drastic disparities in people’s financial realities. For instance, a middle-income household may spend modestly on these services, while a high-net-worth individual’s usage might not be significantly higher, despite a vast difference in income. This narrow range of values fails to capture the true spectrum of financial capability, rendering the data less useful for accurate credit assessments. Sporadic payments or occasional digital transactions don’t paint a full picture of someone’s day-to-day financial health, especially for small businesses that may have multiple transactions per day. A handful of data points gleaned over weeks or months can’t reliably illustrate stable cash flow, making it difficult for lenders to separate low-risk customers from high-risk ones. Without consistent, detailed financial insights—like verifiable income streams or steady transactional patterns—credit scoring models often rely on incomplete snapshots. This can lead to overestimating the risk of borrowers who would otherwise be sound credit candidates, or underestimating risks for those who lack a proven ability to manage credit responsibly. In both cases, lenders and borrowers alike suffer from a scoring approach that isn’t grounded in the realities of daily financial behavior. While telco usage or utility bills may reveal a snapshot of consumer behavior, they rarely present a complete financial portrait. The real clue to creditworthiness often lies in transactional data: Bank Statements: Patterns of deposits, withdrawals, and consistent monthly balances reveal far more about a borrower’s ability to manage debt. Purchase Histories: Frequent purchases and repayments can signal consistent cash flow, helping banks spot stable customers among those with limited collateral. Machine Learning Insights: A robust machine learning model can unearth patterns—like cyclical inflows from a business or regular savings—that don’t appear on a traditional credit history report. For SMEs and consumers alike, these transactional footprints offer a clearer, data-driven indicator of a borrower’s financial health than less frequent, less telling “alternative” data points. AdalFi, a leader in the credit scoring & digital lending space recognizes that the fundamental issue isn’t a lack of data—but the failure to harness the right data in the right way. Their approach focuses on building advanced machine learning models around transactional data, delivering a more nuanced picture of a borrower’s risk profile. By analyzing the ebb and flow of account balances, deposits, payments, and transfers, AdalFi’s algorithms capture the real-world dynamics of personal and business cash flow. This granularity helps lenders offer credit to viable borrowers previously disregarded by standard systems. Rather than competing with established institutions, AdalFi partners with them. Through seamless integration into existing banking processes, these solutions enable lending teams to move beyond outdated checklists. Smaller businesses like Usman’s grocery store or ambitious graduates like Zainab finally get a fair shot at credit. Traditional credit applications in Pakistan can drag on for weeks due to manual checks and disparate scoring criteria. By centralizing and standardizing transactional data analysis, AdalFi aims to cut approval times—benefiting lenders with faster decision-making and borrowers with quicker access to capital. Transforming credit access in Pakistan isn’t solely about giving individuals loans; it’s about empowering a thriving ecosystem: Economic Ripple Effects: More small businesses securing capital means greater employment opportunities, increased consumer spending, and overall economic growth. Collaboration Over Competition: Banks and fintechs can leverage each other’s strengths—established trust meets technological innovation—leading to more diverse, flexible financial products. Confidence in the System: As approvals become more data-driven and transparent, borrower confidence grows, paving the way for broader participation in formal financial services. Pakistan stands at a crossroads where technological capacity can finally meet real-world lending needs. The country’s unbanked or underbanked segments represent enormous potential for growth—if the financial sector can adopt systems that accurately gauge their creditworthiness. No single solution can solve the nation’s lending woes overnight. Yet innovations like AdalFi’s machine learning models—focused on transactional data—point the way toward a more transparent, data-rich lending environment. By prioritizing verifiable, frequent financial signals over incomplete or misleading “alternative data,” banks and fintechs can make fair, informed decisions. Ultimately, bridging Pakistan’s credit gap means bridging the trust gap: giving hardworking individuals and SMEs the financial boost they need to realize their potential, and giving lenders the reliable metrics they need to manage risk. It’s a long road, but the destination—widespread financial inclusion and a vibrant, resilient economy—makes the journey worth every step.