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Artificial intelligence rules cash and banks

Published on February 18, 2020 by Dilinie Gunathilaka

Artificial intelligence (AI) has created a buzz all around as it has significantly impacted society including businesses. Its applications are not limited to only robotic technology, but also encompass data processing and decision-making based on analysis.

Banks process large amounts of historical and real-time data to arrive at a cash-optimisation decision. However, human analysis of large quantum of data is prone to errors; besides, the process is too complicated. Having accurate and quality data is very important, while having the ability to process large amounts of data with advanced analytics is critical. AI gives banks the flexibility to analyse large quantities of data accurately in a short period.

AI uses algorithmic decisions based on statistical evidence to validate its certain recommendations. Software robots that can gather real-time information may be integrated into banks’ operating systems to collect data on services, i.e. cash deposits and withdrawals at branches or ATMs. Further, both internal and external data sources can be integrated at speed to arrive at more realistic and better decisions. Intelligent systems can quickly learn specific patterns of customers and their behaviour in different situations. For example, they can evaluate the demand for cash during different days of a week and at different times of a day. The algorithm itself will design optimisation algorithms based on its findings and using the information collected, a forecast will be made on the need for cash at different ATMs and bank branches.

Intelligent systems are able to create adaptive and flexible prediction models using historical data. The behaviour of a system is stimulated, and factors affecting certain fluctuations in behaviours/demand for cash/availability of cash will be identified. The activation of the intelligent optimisation algorithm takes place after the identification of controlled object behaviour. This process helps banks identify its optimal cash requirement under different scenarios and will likely make their cash management more effective and accurate.

Banks’ cash management does not operate in silo. They have direct links with other departments such as treasury, compliance and marketing. AI has been helpful in establishing effective direct links with these departments, which support real-time functioning.

The use of AI in banks’ cash management not only helps make the right decisions but also helps reduce operating expenses, largely by trimming the workforce.

One of the main barriers to the implementation of AI is the availability of quality data. To overcome the challenge, banks need to have a clear strategy for sourcing data and sharing information across banks in a regularised manner to minimise misuse of data. The adoption of AI has been limited owing to the lack of skilled and experienced technical individuals. However, Acuity Knowledge Partners can leverage its expertise to assist clients as a managed service provider in handling large amounts of data. The large amount of initial capex involved has been another limitation that has held banks and other organisations from using AI. Integration challenges of different systems also act as a barrier to the speedy implementation of AI across banks, leading to longer-than-expected implementation times.

Source:

https://www.cashanalytics.com/how-will-machine-learning-artificial-intelligence-and-automation-help-accounting-and-treasury/

https://www.atmmarketplace.com/articles/how-ai-can-save-your-fi-on-atm-cash-management/


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About the Author

Delivery Manager, Commercial Lending

Dilinie Gunathilaka is a Delivery Manager at Acuity Knowledge Partners’ Commercial Lending team in Colombo. Previously at Acuity, she was an Analyst in equity research and fixed income research. She has over 13 years of experience and holds a MSc in Management (Finance, Special) from the University of Sri Jayewardenepura. She is also an Associate Member of CIMA UK and a CFA Level III candidate.

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