Artificial Intelligence in Banking | Benefits and Best Practices

Introduction to AI in Banking

Artificial Intelligence is a kid who is pursuing now in their teenage. Every field (marketing, manufacturing, healthcare BPO, etc.) accepts Artificial Intelligence. The banking sector is a field that welcomes artificial intelligence with open hands. AI in Banking is a joint process powered by chatbots (which are already evolving daily) and other automation technology, and for giving life to these techniques, machine learning and deep learning play a vital role.

 

According to the report "Accenture Banking Technology Vision - 2018", 83 % of bankers in India believe that Artificial Intelligence will be their companion in work in the next two years. So, this matter of the banking sector using Artificial Intelligence will provide the answers to the following questions -

• What are examples of AI applications already being used by bank employees and customers?

• What will these applications' advantages be regarding time efficiency, cost efficiency, and effort?

• What is the future scope of AI in the banking sector?

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Example of a Framework - Banking with AI-powered by AutoML

How can AI be a part of the Banking Sector? What is the correct place to put the different applications? These questions come to mind when two terms—banking sector and AI are put together. Below is a framework that can be considered an example of the collaboration between AI and the banking sector, handling different services using the separate application of artificial intelligence. Some of the components in the diagram seem to be unknown or unseen; their context has been introduced briefly in the applications sector, but one term, AutoML, appears to be new.

 

The banking sector's services demand automation as there is a requirement to handle different functions simultaneously for many customers with good accuracy and precision. Using AutoML instead of ordinary ML can be a good option for serving that purpose. Let's tackle AutoML briefly. Automated Machine Learning (aka AutoML) is a way of pursuing Machine Learning, and methods and processes can also be used by persons who do not know about Machine Learning. The tasks that AutoML could handle are -

• Preprocessing the data

• Feature selection and Features Generation

• Model building and selection

• Optimization of the model hyperparameters

• Post Processing of the models

• Analyzing the results

Tools that can be used for AutoML are AutoWEKA, Auto-sklearn, H2O AutoML, MLBox, etc.

What are the Applications of AI in Banking?

For Artificial Intelligence, banking is like an ocean of opportunity. Some of these applications are briefly described below. For ease of understanding, these applications are divided into their subcategories. These applications and subcategories are 

Business Process Management at Back-End

• Human Resource Related Services - Artificial Intelligence in banking can be used for handling primary stage tasks related to hiring, such as engaging with recruits, initial stage filtering using social media analytics, and pre-screening the candidates over chat Risk Analysis.

• R&D of Investment Related Services - There are many repetitive back-end tasks. Using software robots to handle such tasks can be a good option, not only saving time but also providing excellent efficiency and accuracy.

• Algorithmic Trading - Many algorithmic solutions are used for handling high-frequency trading where data is imported from various financial markets, and based on this data, several investment decisions are made in milliseconds.

• Robotic Process Automation - Cognitive computing is the future of Robotic Process Automation.

• Insurance Under-Writing - Using Artificial Intelligence to handle insurance-related tasks such as risk assessment accuracy measures and predicting the premium to be paid by a customer.

Privacy, Security and Compliance

• Scam and Fraud Detection and Prevention - Using machine learning (a subset of AI) for scam and fraud detection is very comfortable now, unlike the measures used in the past.

• Compliance Monitoring - The use of AI reduces the time taken to examine the lengthy documents and marking the potential issues, and now it is possible in seconds as compared to hours previously.

CRM, Marketing and Customer Support

• Chatbots or Voicebots Services - Chatbots and voicebots are now famous, and more advanced chatbots are now known as Co-bots (chatbots with cognitive capabilities ).

• Smart Wallets - E-wallets with quick and intelligent capabilities, such as fingerprint scanning for security purposes, make them easy and secure.

• Personalized Financial Services - Bots with intelligence capability are also used to manage customer targets, such as recommending stocks or bonds.

• Robotic Process for Handling Financial Products - Financial Products can be handled using robots with zero human intervention.

Enabling Artificial Intelligence in Indian Banks

India is on track to become a global hub of technology. The Banking sector of India is also adopting Artificial Intelligence and its techniques. Let's consider some examples of the same - State Bank of India (SBI) has already built a solution based on Artificial Intelligence, which is developed by a team (winner of the first hackathon arranged by SBI). From the words of Sudin Baraokar, SBI's innovation head - "The solution essentially scans cameras installed in the branch and captures the facial expressions of the customers and immediately reports whether the customer is happy or sad - this is real-time or near real-time feedback." Senseforth AI Research for HDFC Bank has developed a chatbot based on AI "Eva." The full form of Eva is Electronic Virtual Assistant.

According to HDFC, Eva has already addressed 2.7 million plus queries from 530k users. In the quest to launch an AI-based chatbot, ICICI Bank is not lagging in any manner. The chatbot, which ICICI Bank launched in February, has already answered about 6 million queries and maintained a reasonable accuracy rate of 90 per cent. This chatbot is known as iPal. Not only Indian Bank but also international financial institutes such as JPMorgan Chase and Wells Fargo are investing some of their budgets in AI. In 2017, JPMorgan invested 3 billion USD in new initiatives, such as AI.

What are the Best Practices for AI in Banking?

The best practices for AI in Banking are listed below:

• Understanding the Specific Problem by Identifying the Particular Business needs - Knowing what the business needs is necessary. First, Artificial intelligence can provide different solutions for the same problem, but it is essential to see the disease before prescribing any medicine.

• Develop a Management Strategy for Handling Data - Banking is a field without data scarcity. In fact, in banking, processing an enormous amount of data is a problem. So, it is better to maintain management planning to clean, extract, and centralize the data. After that, the data should be structured into a form that is understandable by AI.

• Giving time to AI for Self-learning - Learning is the most critical aspect of any AI technology. AI is a technology that requires a lot of learning to deliver a good result; it is not a software program that will provide excellent results as soon as it is deployed. It requires being fed historical data and trained itself, which can be time-consuming.

• Automating the Testing Continuously - Having correct results in the development phase by AI does not ensure that it will give accurate results in production either. It can provide undesired effects on the actual data. That is why it is essential to set up a mechanism for continuous testing for AI.

• Maintain the Correct Mathematical Spirits of the Solution - The solution provided by an AI should be mathematically and practically correct.

  Benefits of Artificial Intelligence in Banking Sectors

1. Operational Efficiency and Risk Reduction

AI streamlines processes, cutting down operational costs while minimizing the potential for human errors, thus enhancing risk management.

2. Elevated Customer Experience

Through AI-powered chatbots and virtual assistants, banks can offer personalized product recommendations, exclusive offers, and tailored services, thereby enriching the overall customer experience.
3. Fraud Detection and Security Enhancement

AI enables real-time identification and prevention of fraudulent activities, bolstering security measures to safeguard both financial institutions and their customers.
4. Regulatory Compliance Automation

AI assists in automating processes, ensuring adherence to regulatory standards and mitigating the risk of major defaults, thus helping banks to comply with industry regulations effectively.
5. Enhanced Operations and Cost Reduction

By optimizing operations, reducing expenses, and automating compliance procedures, AI contributes to enhanced efficiency, lowered risk, and quicker decision-making within banking institutions.
6. Improved Decision-Making Processes

Leveraging AI-driven insights, banks can make better-informed decisions, streamline operations, and further enhance cost efficiency, ultimately fostering improved operational outcomes.

What are the Challenges for Enabling AI in Banking?

Artificial Intelligence implementation requires privacy policy like GDPR (General Data Protection Regulation), which is introduced by Europe for its citizens. The next big challenge is the lack of human resources. It is already stated above that AI now is in the teenage stage but AI still lacks in the capable hands which can handle the core technologies in right and efficient manner. The threat to the employment generated by the use of Artificial Intelligence is another challenge. Though AI opened the door for new job opportunities such as Data Science and Data Engineering, on the other hand, it is also true that the adaptation of AI may also cause an unemployment problem in the sector.

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