ML backed by AI to process thermal print images using OCR could automate the process of reading and registering the entries running into lakhs of entries, leading to precise authorization and reducing errors
This is an exclusive interview conducted by the Editor Team of CIO News with Pushkal Tenjerla, Director-IT & CISO at CMS Info Systems
Artificial intelligence is a technology that enables a machine to simulate human behaviour. Machine learning is a subset of AI that allows a machine to automatically learn from past data without explicitly programming it, especially for delta changes.
A successful organization’s ultimate focus would be customer experience in some form or another, whether B2B, B2C, or D2C. With the ever-evolving demands of the customer, the cash management industry is also in a situation where its ultimate focus is on customer experience.
The ATM cash industry, including the banks, MSPs (managed service providers), and secured logistics, is rapidly deploying machine algorithms to automate time-consuming, mundane processes such as reconciliation, reporting, and offering a far more streamlined and personalised customer experience.
The relevant use case is that of reconciling the transactions executed on the field as a part of the daily cash replenishment exercise in ATMs country wide. This, in several cases, is done manually using dual entry of transactions; once on the field with photographic evidence as images using a mobile app, and in some cases, later in the back office as an authorization entry. This process is time-consuming and tedious and error-prone, owing to the nature of manual reconciliations. Repercussions could be the high threat of huge financial penalties.
ML backed by AI to process thermal print images using OCR could automate the process of reading and registering the entries running into lakhs of entries, leading to precise authorization and reducing errors.
Machine learning models can be of great help to the cash management industry when it comes to analysing current demand trends, automation, predicting cash requirements, ATM site requirements, route optimization, fault prediction, and proactive fault management to ensure minimal downtime since machine learning works by extracting meaningful insights from raw sets of data and provides accurate results without having to re-program all over for every little variation.
Risk management is highly aided by AI/ML where a systematically trained model can help predict high risk probabilities and potentially reduce fraud.
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