About this Book

There are different uses that artificial intelligence-enabled technologies are offering to the banking industry. They include but are not limited to, improvisation in customer – service, optimizing processes, providing personalized services to customers and clients, etc.

There are a number of banks that are incorporating AI into their business. Large commercial and investment banks are rapidly integrating technologies like ‘AI and Blockchain’ in their back office as well as customer-facing processes. In most of the countries, adoption of these technologies at such a wide scale has not happened. The banking sector has now started adopting AI, but it is still in its budding stage. The applications of Artificial Intelligence (AI) and Machine Learning (ML) in data analytics and customer service create an opportunity for more personalized and faster customer experiences, better insights, and automation of back-end workflows.

There are various artificial intelligence technology systems that are driving the recent advances made in this sector. These technologies include robotics, computer vision, language, virtual agents, and machine learning.

These systems are evolving in an environment that is filled with technological and digital disruption happening at a wide-scale. This has led to the leading banks investing in artificial labs and incubators. Also, they are hiring and designating people in the roles like ‘Chief AI Officer’ or ‘Head of AI’.

The banks are focusing on various tools to make banking operations more viable in today’s digital world. These tools need to have those capabilities that can match up to the lifestyle trends of modern human beings. To fulfill these changing needs, the banks have started offering tools like ‘AI-powered bots; Intelligent Personal Investment Products’, etc. Many banks are moving towards custom in-house solutions that can use technologies like Natural Language Processing (NLP), Machine Learning (ML), Pattern Recognition, and Probabilistic Reasoning Algorithms.

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Speech recognition, for instance, can be trained to understand what customers speak. The bots using ultrasonic sensors can move to guide the customers inside a bank branch. Face detection algorithms can enable these bots to recognize the customers. Natural Language Processing (NLP) system offers simplicity and convenience of a voice-based interaction in banking transactions. It can also help in a better understanding of customers’ queries.

NLP can further enable these bots with superior linguistic capabilities, where they can understand and respond to customers speaking in multiple languages.

Natural Language Processing (NLP) technology can be useful in detecting patterns and anticipating customer needs. It can help the banks with their Customer Relationship Management (CRM) and Decision-making system. NLP can capture content from customer interactions done through various channels like emails, text messaging, social media posts, etc. This data can then be used by Machine Learning (ML) tools to recommend relevant and consistent actions that would ensure customer engagement.

Similarly, AI-based tools can help banks to realize a more personalized, one-to-one, and outcome-focused approach. AI and ML, together create that capability to analyze hundreds of individual customer data points in a fraction of a second. This analysis helps in determining the optimal approach or action that would be most relevant for an individual customer at a particular moment.

AI implementation is becoming increasingly crucial because banks, today have an urgent need to reduce costs, meet margins, and exceed customer expectations. There are certain factors that have opened up a vast playing field for artificial intelligence in the banking sector. Some major factors are huge advances in mobile technology, data availability and the explosion of open-source software. In the present app-driven world, it has become imperative for the banking industry to embrace AI and integrate it into its business goals.

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According to Gartner, a global research and advisory firm, by 2020, the consumers will manage 85% of their total business interactions with banks through Fintech chat-bots.

Juniper Research (mobile, online & digital market research specialists), estimates that the introduction of chatbots and virtual assistants will save companies $8 billion per year by 2022. The banks also will be able to reap such benefits offered by these technologies.

Both these forecasts may seem to be a cause of worry for the banking workforce, but many experts think otherwise. These experts are confident that the banking employees will not lose their jobs by being replaced by such AI and ML solutions. They suggest that these tools will help in releasing certain pressure points and in fact, empower the employees in many ways. Accenture (a multinational professional services company) has a very encouraging and positive prediction for such a scenario. It says that the banks that deploy AI wisely will see a 14% increase in jobs.

In terms of improving customer experience, the chatbots are currently the most visible form of AI being adopted across the banking sector. This is an obvious outcome of a reality that a big segment of customers prefers the convenience of using its mobile phones and laptops instead of having to visit a branch. Through machine learning, these chatbots are constantly improving their ability to accurately identify the customers’ issues and provide them with appropriate solutions. The chatbots are now practically an industry-standard as there are several instances of these tools successfully performing various tasks and on the way to improving their capabilities to perform more complicated tasks. “CEBA”, an in-house bot developed by Commonwealth Bank of Australia (CBA), has the capability to perform around 500 tasks. It is also expected to successfully segregate and recognize 500,000 different ways customers may ask for different banking activities. Another relevant example is “Nina”, Swedish Bank’s AI chatbot, which can answer various customers’ queries on its own and has the capability to identify and pass on the more complex inquiries to the members of staff.

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Apart from facilitating customer-engagement, AI is being used to enhance certain other areas in banking activities. AI has the potential to help banks in ‘Compliance; Detecting Fraud and Money Laundering; Process Automation’ etc.

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