In a recent demo call, we walked a mid-sized NBFC through our bank statement analysis features.Their CTO nodded enthusiastically talked about fraud detection features, cash flow analysis, and  automated categorization. 

Then came the question about AI-powered credit decisions. "Can your system automatically approve loans based on this analysis?" 

Here was a team that understood data, appreciated good technology, but they had the same concern everyone in our industry does: the gap between having AI tools and trusting them with decisions that could make or break their business. The problem with this question is not that it isn’t possible but that it’s a little bit far into the future when we haven’t yet solved some key underlying issues to make precise decisioning possible – even with humans, forget trusting algorithms with it.  

The data quality paradox 

India's digital infrastructure story is genuinely impressive. UPI processes INR 23.24 lakh crore annually. Aadhaar covers 136.65 crore Indians. Our Digital Public Infrastructure makes headlines worldwide. These are remarkable achievements that have transformed how Indians interact with financial services. 

Yet, beneath these successes lies a challenge that predates our AI ambitions: data quality and consistency across the financial ecosystem. 

Working with dozens of lenders has given us a front-row seat to this challenge. Most institutions face similar data realities. Systems that don't communicate effectively. Datasets are scattered across platforms. Compliance reporting varies because different agencies have different requirements. 

FIDC's recent request to RBI for a single reporting window highlights what we see daily: base-layer NBFCs juggling parallel reporting to multiple Credit Information Companies. The resulting data inconsistencies affect everything downstream. 

The jarring question around using AI for financial decision-making is that if standardizing basic financial data remains complex, how do we ensure that AI models trained on this data can make reliable decisions? 

The infrastructure mirage  

The data ecosystem in India is evolving. Public repositories are maturing. Proprietary datasets come with usage considerations and compliance requirements. The governance frameworks are developing alongside these capabilities. 

However, poor and inconsistent data quality continues to be a roadblock, costing organizations more than  $12.9 million annually, according to some estimates. For Indian NBFCs operating in a competitive environment while navigating evolving regulations, this represents both an operational and strategic consideration. 

Why automation wins (for now)  

Here's what we've learned from building lending infrastructure – AI-driven automation delivers substantial value without requiring perfect infrastructure and data pipelines. 

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Many financial institutions today have already transformed their operations with AI applications that work reliably. Our bank statement analyzer, for instance, can process months of transaction data in seconds, extract insights about cash flow patterns, identify irregular transactions, and categorize expenses automatically. The AI doesn't need to understand the cosmic truth about a borrower's character. It just needs to accurately read and analyze what's there. The rest of the work is upon the lender or the underwriter to set up the right parameters to separate signal from the noise. 
 
Pattern recognition for fraud detection thrives even with imperfect datasets. Compliance automation streamlines regulatory reporting without making complex risk judgments. 

A crucial characteristic these applications share is how they enhance human decision-making and deliver better data, faster processing, and clearer insight. 

Where AI creates real value today 

We've seen lenders reduce document processing time from hours to minutes. Customer onboarding that used to take days now happens in real-time. Compliance reports that required weeks of analysis are now generated in seconds 

But here's what's more interesting: every automated process creates cleaner data as a byproduct. Every standardized workflow reduces inconsistencies. Every compliance automation builds better governance frameworks. 

So, you're not just improving efficiency. You're building the data foundation that future AI-driven decision-making will be based on. It’s not just effective but future-proof too.  

 The decision-making divide 
 
From our experience building these systems, we've learned that full automation of credit decisions by AI introduces risks that most institutions aren't equipped to handle yet. The regulatory framework is evolving. The explainability requirements are stringent. The cost of errors can be institutional. And when loan decisions face scrutiny, lenders need explanations that satisfy regulators, courts, and customers. 

Data quality issues can amplify bias in ways that can create serious institutional challenges. In financial services, this can lead to unintended discrimination that regulators are increasingly focused on. 

The explainability challenge is particularly complex in lending. Unlike fraud detection, where false positives can be reviewed, credit decisions have immediate impacts on people's lives and businesses. 

When ready means ready 

We're seeing global financial institutions take measured approaches to AI-driven decision-making. This piece isn’t advocating a censure of the idea that AI can make decisions; it’s only meant to posit that there’s a lot of other meaningful groundwork that must happen before we get there.  

 Better data quality. Stronger governance frameworks. Clearer regulatory guidelines. More sophisticated risk management. 

Sustainable AI adoption requires patience, preparation, and a clear understanding of where AI adds value versus where it introduces risk. Meanwhile, the automation opportunity can transform operations, reduce costs, and improve customer experience. 

The technology exists. The infrastructure can be built. The smartest institutions are already moving — automating grunt work and pesky operations today while strengthening the data foundations that tomorrow's lending decisions will require.