Artificial Intelligence is out of the buzz bubble and very much present in our reality today. The technology has penetrated into our lives much faster than we had imagined. AI has found exciting use cases in all major industries and sectors. The most vivid of its use cases it the growing use of chatbots. Born to replace humans in repetitive and monotonous tasks, take decisions like humans, and interact with the intelligence that humans possess, the technology is at the peak today.
We are so close to realizing Artificial Intelligence in its complete potential that it is exciting and all the more threatening. Organizations around the world are so sure of the technology strategies that they have begun to keep Artificial Intelligence at the centre.
While all major industries in the world have been hit hard by the wave of Artificial Intelligence, the one that was most disrupted is the Banking and Finance sector. Far and wide use cases of AI have been designed, Offshore Java developed, and almost deployed at various points in the operations of a banking industry.
Some of these include-
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Anti-money Laundering – AML is essentially a set of processes and laws that are designed to halt the practices that generate income from illegal sources. Generally, people involved in money laundering make the process look as though the money was earned from legitimate sources. To combat this, most of the financial institutions are moving from their rule-based systems to AI-based systems that are intelligent enough to detect money laundering patterns.
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Customer satisfaction use case – In a leading Indian bank, a use case for detecting the customer sentiment when and as he interacts with a bank representative has been developed. Cameras installed in the premises read a customer’s facial expressions and thereby decide on the effectiveness of the consultation given to them based on the inference made by an AI-enabled application. This use case is used as a metric for measuring the customer’s satisfaction.
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Chatbots – Chatbots, the artificially intelligent systems that are made capable to interact like humans with humans on the Internet have grown in popularity. The bots employ natural language processing to fathom the human text or voice inputs given to them in terms of their contexts and then respond appropriately. Banking institutions need to address customers’ queries on a large scale every day. The monotonous queries out of these that are similar in nature can be efficiently handled by the chatbots, reducing the work of a human and making it more worthy.
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Risk Assessment – Financial institutions go through a client’s complete profile before they grant them loans to get an estimate of their credibility and worth. Traditional systems processed historical data of the customers to get an idea about their financial profile, which led to inaccuracies in predicting their future behaviour. Machine Learning and AI have teamed up to rid the banks of this method. With ML, real-time analysis of data can be done, the market conditions can be studied, and the latest news can be analysed to get a complete idea of the potential risks involved in offering credit to the client.
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Marketing – With an ability to analyse past customer behaviour, the present and future campaigns can be designed to build targeted campaigns. Data from all over the Internet such as from mobile and websites, the user-generated data, responses to previous ad campaigns, etc., can be studied to drive efficient campaigns and convert most leads into customers. Machine learning can help financial institutions learn their customer’s patterns and ensure that their journey in their organization is smooth and hassle-free.
While these high-level implementations sound easy, a deep outlook on the insights into these technologies is required when bringing them into reality.
