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How auto reconciliation helps banks counter frauds

News & Blog

As technology advancements continue to drive convenient banking avenues, there is an increasing threat of frauds. With persistent cases of cheating, fraud is an inevitable cost of business which banks can’t stop, but only hope to reduce.

According to Institutional Investor Advisory Services (IiAS), India’s domestic banking sector reported a total of 12,553 fraud cases worth Rs 18,170 crore in fiscal 2016-17. Fraudsters are not only using sophisticated methods to hack into accounts, but have also been able to enter banking system as insiders.

While it is near impossible to ensure complete protection against unknown threats, some level of preparedness from banks can go a long way in countering fraud threats. Along with adopting pre-emptive techniques, banks need to strengthen the audit quality by plugging process gaps and streamline the checks and balances.

It is a fact that reconciliation methods have been in place to identify unusual or suspicious banking activities —be it money transfer data, loan transactions, teller cash exports, ATM transactions data, etc. The reconciliation processes help establish accuracy of data between the balance sheet and the bank statement recorded in the cash ledger. However, massive transaction volumes, process complexities, multiple data sources and evolving regulatory requirements have led to cumbersome reconciliation processes. Most financial institutions still use manual reconciliation processes which are often expensive, time-consuming, error-prone and unproductive.

There is a need for organizations to realize the potential of automating reconciliation. There are a number of financial institutions which by adopting auto reconciliation methods are not only benefitting from faster identification of transactions, quicker highlighting of exceptions and real-time identification of variations and miscalculations in data. With increasing technology adoption and penetration of devices by banks as well as customers, the number of data sources have grown rapidly. It wouldn’t be wrong to say auto reconciliation is an absolute need so as to get a consolidated view of data.

Blending strong authentication processes and analytics software, automated reconciliation can greatly reduce exposure to fraud, and offer an added layer of protection. It allows accurate detection of fraud by tracing every transaction to its source through the financial lifecycle— from data ingestion through matching, exception management, reconciliation, certification and signoff.

For example, Ascent Business’ reconciliation engine of banking software enables a single reconciliation job execution through distribution of load across various cluster nodes, thus ensuring optimal utilization of the system resources. It is easily scalable to increased transaction volume and user base without re-deploying and extending the solution.

The platform has the ability to bring significant operational efficiency and risk management to the diverse range activities. What matters more is having a solution with the right understanding of cash flow matching, exceptions and investigation cases.

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