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What makes FinBox BankConnect 10x faster than other bank statement analyzers

Chitwan Kaur   /    Content Specialist    /    2023-01-20

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Despite the vast strides made in digital lending, bank statement analysis continues to be the linchpin on which underwriting decisions rest. But this process, prominently used by legacy credit institutions, has been adapted for automated and intelligent new-age digital lending. 

Technology companies that provide cutting-edge financial servicing solutions have gone above and beyond simply digitizing the process. They are continuously improving the accuracy, resilience and speed of their product. 

At FinBox, once we sufficiently polished our bank statement analyser to achieve high levels of accuracy we shifted focus on enabling it to do its job fast. As a result, FinBox BankConnect now outpaces other bank statement analyzers by at least 10x.

Why is speed important?

Speed is critical in bank statement analysis. It informs turnaround times, and by extension, drop-off rates and customer experience. After lenders have mastered precision and reliability, improving the speed at which their bank statement analyzer performs should become priority.

Lenders are required to assess six to twelve months (or more) of financial transactions before making a decision. Bank statements required for underwriting can run into thousands of pages. Despite advanced digital capabilities, parsing and scanning these documents can take tens of seconds, if not minutes. For an end-user who has been promised a quick digital loan, a minutes-long wait time in a user journey can be enough reason to drop off.

Let’s look at bank statement analysis from the customer’s perspective. The imperative of sharing bank statements alone is a significant deterrent of customer experience. When a long wait time is added to this process that already induces hesitancy among customers, they are more likely to abandon the journey.

As a result, lenders risk losing customers and their conversion rates may tank. Since conversions are an essential top-of-the-funnel concern closely linked with the lender’s topline, the impact on revenue can be adverse.

What makes BankConnect fast?

FinBox BankConnect enables lenders to reduce this turnaround time significantly. It has been designed on the rails of best-in-class technology to reduce the burden on the underwriting function. Let’s look at the ingredients required to build a fast bank statement analyzer: 

Parallel Processing

Lengthy bank statements can overwhelm the underwriting function. Typically, customers end up uploading separate statements for each month. Incumbent bank statement analyzers wait for the customer to share all their statements before they initiate the analysis, compounding the time taken to complete the analysis. 

BankConnect mitigates this wait time in underwriting by processing the statements parallelly, in real time, as the customer uploads subsequent statements. The process reduces latency to extract, clean, analyze and generate excel reports on a 50-page bank statement to just seven seconds.

As a result, BankConnect outperforms its competitors significantly when it comes to speed. As many as 90% of bank statements were analyzed within 1.34 seconds, whereas competitors took 15.3 seconds to perform the same analysis on these bank statements.

Information prioritization

Bank statements are a treasure trove of information. Most bank statement analyzers, however, are overwhelmed by these copious transactions. Underwriters are presented with the challenge of making sense of this data in the fastest and most optimal way. 

Bank statement analysis flow:

Data extraction → securing raw transactions → analysis

BankConnect makes this process agile by prioritizing information in the order in which it is required for analysis. For instance, performing a sanity check is conditional to proceeding with the actual transaction analysis. Only if this preliminary information like name, account number, date range, and accessibility of the PDF (for instance, whether it is password protected) is vetted successfully are the servers invoked to perform the analysis. 

FinBox’s bank statement analyzer enables dynamic extraction and analysis which separates conditional tasks like sanity checks from the actual transaction analysis. This is necessary to speed up the process – the customer is informed whether they passed the sanity check before transaction analysis can take place.

In case some pertinent information is missing during the sanity check, it is flagged to the end user well in time, and not after the analysis that uses up precious resources. Moreover, this analyzer has earmarked appropriate tech resources for the preliminary sanity check and the subsequent bulk of the transaction analysis.

BankConnect’s agility in prioritizing data extraction and analysis helped lenders reduce the wait time for end users. This had a positive impact on their customer experience. BankConnect clocked an 86% conversion rate compared to competitors who registered only 67%. 

Error handling

Given that bank statements are repositories of extensive data, the chances of errors in processing them are also high. For instance, there may be failure in analyzing a single page of a bank statement. The resulting analysis would be incomplete, inconsistent and to a degree, unreliable. Re-analyzing the same statement would drive up turnaround time.

BankConnect uses state-of-the-art tools to minimize the instance of error. If an error is detected during analysis, it is addressed in the first attempt. This bank statement analyzer is alerted of the error and another process is invoked to retry the task immediately.

Conclusion

Digitisation and automation were just the early spoils won from the transformation of bank statement analysis. But since there’s always room for improvement, optimizing digital lending for speed is the next logical step for digital lenders looking to improve their customer experience. 

Investment in system architecture and engineering decisions has allowed BankConnect to reduce the time taken for data extraction. Keen attention is paid to granular choices made in engineering the product – code structure, its configurability, choice of programming languages and libraries, coupling, etc – to remove inconsistencies. 

Its robust tech stack has been honed to deliver results with minimum latency. From the programming languages used and the cloud platform on which the analyzer runs to end-user facing features like an Account Aggregator integration - the entire underlying infrastructure is geared towards offering the fastest underwriting experience in the industry.

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