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How AI and machine learning are changing the banking industry

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How AI and machine learning are changing the banking industry

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Banks can leverage the power of AI and ML in banking, along with data science acceleration, to enhance customer’s portfolio offerings.

How AI and machine learning are changing the banking industry
Digital transformation is extremely crucial given the unprecedented times we are in. To modernise banks and legacy business systems, without causing any disruptions to the existing system is one of the major challenges. However, Artificial Intelligence (AI) and Machine Learning (ML) has evolved as the crucial enabler in conducting hassle- and risk-free digital transformation. An AI and ML-led approach to system modernisation will allow businesses to collaborate with other fintech services into adapting modern demands and regulations while increasing safety and enabling security.
In the world of banking, with the increasing pressure in managing risk along with growing governance and regulatory requirements, it is mandatory for banks to enhance their services towards more exceptional and better customer service. Fintech brands are increasingly using AI and ML in a range of applications across multiple channels to leverage all the available client data to predict how customers’ needs are evolving, what services will prove to be beneficial for them, what kind of fraudulent activity has the highest possibility to attack customer’s system etc. Banks can leverage the power of AI and ML in banking, along with data science acceleration, to enhance customer’s portfolio offerings.
Significant Role of Artificial Intelligence and Machine Learning in Banking and Finance:
  • Mitigate risk management – One of the best examples to showcase the benefits of Machine Learning can be described here. Back in the days, while providing loans to customers, banks had to rely on the client’s history to understand the creditworthiness of that respective customer. The process was not always accurate, and banks had to face difficulties in approving the loans at times. But with the digital transformation, the machine learning algorithm analyses the customer in a better way to process the loan in a much convenient manner.
  • Protecting fraudulent activities – Banks are already one of the most highly regulated institutions and must comply with strict government regulations in order to prevent defaulting or not catching financial crimes within their systems. This is one of the major reasons for the banking processes to go all-digital in such a short span of time. To mitigate fraudulent activity, it is very important to understand the risk before any suspicious activity has begun. During the traditional process, for banks to protect customers from fraudulent activity, they had to violate some pre-set protocols. While ML can sense suspicious activity even before the external threat violates the customer’s account. The underlying benefit from this is, machines can perform high-level analysis in real-time which is impossible for humans to perform manually.
  • The functionality of chatbots – Chatbots are basically AI-led software that clones human conversation. The technology imbibed in chatbots makes it extremely convenient for banks to serve customer queries quickly. The chatbots are proving extremely beneficial for financial institutions to serve large scale customer issues in a matter of a few hours.
  • Algorithm-based marketing – The ability to recognise the past behavior of the customer and to craft targeted campaigns is a boon for both customers and banks. This customised campaign creates all the necessary information the client would require making both save a lot of time and energy. Modern customer also enjoys services that are customised according to their preferences and enhance their banking experience.
  • With the surge of fintech companies and the rapid change in the use of technology, it was a matter of time that AI and ML would enter the modern banking and change the dynamic forever. The use of AI and ML will offer predictive data analysis as banks and financial institutions will look to offer better services with more actionable information such as patterned data sets of customers' behavior and spending behavior.  AI adoption for financial institutions will be the key to gain a competitive edge as they will be able to offer a fast, secure, and personalised banking experience to the customers.
    —Ramki Gaddipati is CTO and Co-founder, Zeta. The views expressed are personal
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