Artificial Intelligence is a kid who is pursuing now to its teenage. Every field (whether it is marketing, manufacturing, health or BPO, etc.) is accepting Artificial Intelligence. The banking sector is a field which is welcoming Artificial Intelligence with open hands. AI in Banking is a joint process powered by chatbots (which are already evolving day by day) and other automation technology, and for giving life to these techniques, machine learning and deep learning plays a vital role. According to the report "Accenture Banking Technology Vision - 2018", 83 % of bankers of India have faith that Artificial Intelligence will be their companion in work in the next two years. So this matter of banking sector using Artificial Intelligence will provide the answers to the following questions -
What are examples of AI applications that are being used by employees and customers at banks already?
What will be the advantages of these applications in terms of time efficiency, cost efficiency and efforts?
What is the scope of AI in the future in regards to 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 are some questions which itself come to the mind when two terms banking sector and AI are put together. Below is a framework which can be considered as an example of the collaboration of AI and Banking Sector, handling different services of banking using the separate application of Artificial Intelligence. Some of the components in the diagram seems to be unknown or unseen, their context has been introduced briefly in the applications sector, but one term AutoML appears to be new.
The services of banking sector demand automation as there is a requirement of handling different functions at once for a large number of customers with good accuracy and precision. For serving that purpose, using AutoML instead of ordinary ML can be a good option. Let's tackle AutoML briefly. Automated Machine Learning (aka AutoML) is a way of pursuing Machine Learning in which methods and processes of Machine Learning can also be used by the persons who do not know about Machine Learning. The tasks which could be handled by AutoML 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 which can be used for AutoML are AutoWEKA, Auto-sklearn, H2O AutoML, MLBox, etc.
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What are the Application of AI in Banking?
For Artificial Intelligence, banking is like an ocean of opportunity. Some of these applications are briefly described below. For the 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 can be used for handling primary stage tasks related to hiring such as engaging with recruits, initial stage filtering using social media analytics, pre-screening the candidates over chat risk Analysis.
R&D of Investment related services - There are so many repetitive back-end tasks. Using software robots for handling such tasks can be a good option which can not only save time, but this idea can provide excellent efficiency and accuracy.
Algorithmic Trading - There are so many algorithmic solutions which 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.
India is on the track of becoming 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 come from 530 k users. In the quest of launching AI-based chatbot, ICICI bank is not lacking behind in any manner. The chatbot which is launched by ICICI bank in February already answered about 6 million queries and maintaining a reasonable accuracy rate of 90 percent. This chatbot is known as iPal. Not only Indian Bank, but international financial institutes such JPMorgan Chase and Wells Fargo also 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 - It is necessary to know what the business needs. 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 where there is no scarcity of data. In fact, in banking how to process 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 data should be structured into a form which is understandable by AI.
Giving the time to AI for self-learning - Learning is the most critical aspect of any AI technology. AI is a technology which requires a lot of learning to deliver a good result; it is not a software program which will provide excellent results as soon as it is deployed. It has a requirement to be fed on historical data and to train itself. This 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 also. It can provide undesired effects on the actual data. That is why it is essential to set up a mechanism of continuous testing for an AI.
Maintain the correct mathematical spirits of the solution - The solution provided by an AI should be mathematically as well as practically right.
What are the challenges for Enabling AI in Banking?
The main challenge for the development of an AI solution is the availability of the right kind of data. Data acts as fuel for the machinery of AI. Though in the field of Banking the availability of data is sufficient in most of the cases still it appears to be a challenge for applying AI in the banking sector. The next challenge comes after the availability of data is its privacy and security. In the technological world of Banking, the security of data demands as much concern as the security of any treasure needs.
360 Degree Finance Approach
Artificial intelligence is helping banks become more efficient in the process of detecting fraud and Robotic Process Automation. For Adopting this approach, we recommend taking the following steps -