Managed ATM services provider CMS Info Systems, is expanding the scope of its machine learning tech solutions beyond ATM management to sectors like multi-brand retail, and quick commerce, as it attempts to position itself as a business solutions company, a senior executive told FE.
The applications it has deployed so far span inventory management at quick commerce dark stores, and cash collection and reconciliation at retail outlets.
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“So far, our focus has been on the banking sector where our managed ATM services have a strong foothold. But we have ambitions of becoming a business solutions provider, and not just a business services provider. Now we are looking to extend our tech solutions to NBFCs and retail,” Rajiv Kaul, chief executive at CMS Info Systems said.
As part of its portfolio of services for managing ATMs, the firm developed a machine learning led surveillance system which detects anomalies in patterns at an ATM, and alerts the command centre if the problem persists.
For example, the security system is trained on patterns like the average time spent in an ATM by customers. In case a customer is in the ATM cubicle for longer than usual, the system sends a prompt to the customer, and if the behaviour does not change – in this case the customer does not leave – the system sends an alert to the command centre and to the bank’s nearest branch.
“Our command centre in Mhape process all such alerts. For a new client we have onboarded, we are setting up a command centre on their campus itself,” Kaul said. The new client he added is a public sector bank, but refrained from revealing the name citing confidentiality clauses.
Extending the surveillance tech to quick commerce, the firm offers inventory management solutions at dark stores for quick commerce platforms. In this case, the system helps track inventory using tags which can be scanned when moving.
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The firm has also developed tech solutions that help cash movement for retail chains. In this case, it includes cash reconciliation using technology, reducing the margin of error, and increasing efficiency by automating the counting process.
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