Today’s banking has gone far beyond the traditional brick-and-mortar department banking we were familiar with until a decade ago. Customers queuing at branches, vouchers, a plethora of documents, commotion and confusion, which were once synonymous with the department of any financial institution in India, have become a widespread component due to the use of technology in the banking sector. Starting with computerization, the Indian banking landscape has changed dramatically over the last several decades, with the entire economic game being embedded in our wallets and we managing and running our banks ourselves. With robot method automation, natural and colloquial language processing, effective statistics analysis, predictive analysis and photo analysis, the current AI mechanism is set to become more dynamic, improving the customer experience in many ways. In addition to presenting facts about banking games, chatbots and digital assistants will provide greater support to customers in making financial decisions on their behalf.
Introduction
Digital transformation is one of the most well-known important drivers of the way agencies deliver value to their customers in a competitive, rapidly changing business enterprise environment. Artificial Intelligence (AI) is now mentioned as one of the most important virtual transformation enablers in a wide range of industries at large. Artificial Intelligence (AI) has the potential to facilitate enterprises. They can become more imaginative, versatile and adaptable than ever before. AI is already being implemented to enhance productivity and competitiveness, as well as advancing virtual transformation in many organizations.
Ever since John McCarthy described AI as “the technology and engineering to build smart robots”, it has been around for decades (A.S. Ramasastri, 2020). AI is the collection of statistics, algorithms, and processing power that allows robots to mimic human abilities and act intelligently. Robotic Process Automation (RPA) and Artificial Intelligence (AI) – play an important role in empowering banks to quickly automate their business enterprise operations and efficiently remodel the entire business enterprise.
AI is developing rapidly because there is a technology for corporations around the world to optimize the experience for individuals (Wijai. C, 2019). This technology is getting better and smarter every day, allowing more and more regions to use AI for different applications. The banking enterprise is becoming one of the first industries to hire AI. Banks, like other industries, are experimenting and using technology in many ways. Bringing smart chatbots to customer service, personalizing offers for individuals, or even deploying AI robots for self-service in banks are some of the simpler uses of AI.
Artificial Intelligence in the Banking Industry
Today, AI is being used almost everywhere it can be useful, from the front workplace to the middle and back offices (Alex Kwiatkowski, 2020). According to a 2017 PWC Fintech India study, worldwide AI software spending reached $5.1 billion in 2017, up from $4 billion in 2015 (Sindhu, J, 2019). A study record published in Business Insider shows that through switching to AI banking systems, banks could save an expected amount of $447 billion by 2023. (IDRBT, 2020).
83% of Indian bankers believed that Artificial Intelligence should work together with humans. The worldwide average was 79%. In addition, 93 percent of Indian bankers said they used statistics to make decisions (Accenture, 2018). Banks have been at the bottom of AI for a long time. Customers who have used virtual assistants and a growing variety of AI-enabled career technology can enjoy the impact of Artificial Intelligence in banking (Alex Kwiatkowski, 2020).
Use of AI in banking
Banks and economic establishments are using AI technology, including cloud computing, block chain, machine learning, APIS and robotics, to reduce costs, increase efficiency, improve security and enhance the customer experience. Most large and global banks are incorporating AI for every workplace and customer behavior purposes. Many large economic offering corporations are already working on proof of idea and implementing multiple generation in development, including cloud computing, block chain, device studies in their operations (PWC, 2020). Examples of AI use in banking can be broadly classified into 3 categories: i. Customer experience ii. AI Strong staff and iii. AI powered insights.
Application Of AI In Indian Banking Industry
AI has many programs in the banking industry. Here are AI’s most important programs in banking to revolutionize the industry.
1. Chatbot – AI in banking is more than just a chatbot. Chatbots are AI-enabled conversational interfaces (Magger, 2020). Bots negotiate with hundreds of buyers under the auspices of the bank at no great cost. For example, BI is currently using the SIA chatbot, an AI-powered chat assistant developed with the assistance of Payjo, a startup based in Silicon Valley and Bengaluru (Dennis Ostapchenya, 2020).
2. Mobile App – Mobile App AI capability has become more responsive, optimized and advanced. Banks generate 66% more sales from mobile banking customers, while customers go to branches (Dennis Ostapchenya, 2020). Example: Alexa is an AI-powered digital assistant from Amazon. It was used by IndusInd Bank to connect with its customers.
3. Machine learning – Banks can dramatically reduce the risk threshold by reading a lot of statistics using system learning techniques. ML algorithms can accurately determine who is prone to default on their loans. Machine learning allows banks to be aware of fraud, reduce the chances of mistakes, human mistakes, and automatically and simultaneously check more than one situation (MarutiTechLabs, 2020).
4. Robotics – Banks implement Robot System Automation (RPA) to automate guidance enterprise steps if you want to be aggressive in the market (Rahilaretiwala, 2020). Banks use RPA to enhance operations and control with a good variety of transactions, KYC, and onboarding requests (Diceus, 2021).
5. AML and Fraud Detection – Many banking establishments internationally are already using the system to assist them in migrating from rule-based software program structures to AI-based structures that are even more effective and smarter in detecting anti-cash laundering and fraud activities (Devendra Mangani, 2017).
6. Customer Recommendation Engine – Financial Institution’s advice tool filters statistics using multiple algorithms and recommends maximum applicable goods or financial institution services to bank customers, including credit card plans, investment strategies, funds, etc. (Shivangi Maheshwari, 2021). Example: Easylon – a Mumbai-based startup venture, recently launches India’s first domestic mortgage options and advice engine for domestic mortgage borrowers. Easilon also ties up with major financial corporations and banks which include HDFC, ICICI, SBI, Bajaj Housing Finance, PNB, IIFL Home Finance and others (PressTrustOfIndia, 2021).
7. Algorithmic Trading – Algorithmic Trading or Algo Trading will speak to any change that is initiated using a software program that employs automated execution logic (Rabi Shankar, 2018). Algorithmic buying and selling is the practice of using technology to buy and sell shares. The computer itself analysed statistics including prices, sector, companies and sector order more significantly faster (Deepak Shenoy, 2020). Global banks have already started tying up with Indian exchanges and adopting algo buying and selling. But, Indian banks are below the evolutionary level to implement it (Anupriya Gupta, 2019).
Role Of AI In Indian Banking Industry
Indian banks are gradually using futuristic technology to serve new age buyers and enhance their reform potential. AI is helping Indian banks to improve their operations, be it accounting or income, contracts or cybersecurity (Meha Agarwal, 2019).
1. Chatbot: AI has been added to Indian banks since 2016. Chatbots like SBI – SIA, HDFC – EVA, ICICI – iPal, Kotak Mahindra – Keya, Axis Bank – Axis Aha and Yes Bank – Yes Robot are some of the chatbots used in major Indian banks since 2017-18 (Subuddhi.S, 2019). State Bank of India’s chatbot SIA facilitates general duties to customers such as financial institution representative. The SIA is set up to address 10000 requests per second or 864 million requests per day (Businessworld, 2017).
2. Robots: In 2016, ICICI Bank followed the robot. Which carries out about two million transactions every day (Mukbil Ahmed, 2018). ICICI Bank’s software robot has reduced response times for customers by up to 60% and upgraded accuracy by up to 100%, tremendously increasing the productivity and effectiveness of the lender (Exito, 2020).
3. City Union Bank – Lakshmi (2016), Canara Bank – Mitra & Kandy (2017), and HDFC – Interactive Robotic Assistants are some of the other most important banks that have deployed robots for banking duties (2017). These robots are used in retail, wholesale-sales banking, currency, treasury and global trade (good sense.S, 2019) is applied for a number of functions, including.
4. Machine Learning and Algorithms: On Nirav Modi Fraud with PNB – PNB said it has no tolerance for unethical practices within gadgets and decided to set up AI algorithms for reconciliation of accounts (Bijaya Das, 2018). Mobile App: Allahabad Bank has released AI-based app (Vivek Kumar, 2021), known as “Empower”, while IndusInd Bank recently released an Alexa capability called InduSist, which allows bank account customers to play financial and non-economic games with Amazon’s digital assistant Alexa (Meha Agarwal, 2019)
5. Digital Branch – In March 2019, Bank of Baroda opened a high-tech virtual department at MS University Campus in Vadodara (PTI, 2018), which includes revolutionary gadgets, including an AI robot called Baroda Brainy and a virtual lab with free Wi-Fi access (Prashant Rupra, 2019). In India, there are 12 banks that have gained regular media interest for their AI projects in recent years. To share AI gadgets, the banking sector in India should form a consortium. Cooperatives and nearby banks may be able to search with AI for lower fees and with more security. Growing customer-based fully human robots that can handle the project of guiding customers in a banking manner is the destiny of growing AI in the banking sector.
Challenges In Implementing AI In The Banking Industry
According to a 2019 survey, organizations used AI at a rate of about 37 percent in 2019. In the course of just 4 years (10XDS Team, 2021), the deployment of AI has increased to 270 percent. However, it is important to stress that AI still faces some problems. Here are some of the most common problems that most banks have when trying to install AI.
(i) Lack of trained human resources- The biggest problem is the lack of educated human resources; the modern structure of staff is unpredictable with the most existing technology and programs (Kulbhushan, 2018).
(ii) High costs – The cost of building and renovating AI was exceptionally high and complex. AI is composed of state-of-the-art software programs and programs, which must be updated on a regular basis to meet the needs of a changing environment (Padmanabhan and Princimetilda, 2021).
(iii). Quality of data sets – Any threat caused by unconfirmed data is the main source of problem for organizations. Example: Risks of using AI device for KYC compliance when data supply is incorrect (Kriti Chandrasekhar, 2018).
(iv). Security and storage – AI programs use large amounts of data to make wise decisions. Using large amounts of data can create storage challenges, and data-driven automation can create data security concerns (10XDS team, 2021).
(v). Lack of infrastructure – Replacing outdated banking structures remains a major problem. AI programs that use extreme computational speed. It is expected that many major non-governmental banks will find it difficult to integrate AI because their structures are 20-30 years old (Qnetbukbezsee, 2020).
(vi). Trust in AI – Adopting any new concept requires a high degree of confidence in it. The fear of AI remains through many banks, organizations, customers and clients. AI is incredible, especially in the banking and fintech industries, where money is at stake (Swapna, 2020).
(vii). Others – In addition to the above, AI integration into existing structures, education of complex algorithms and AI models, unemployment concerns, reluctance of financial institution staff, useless implementation of multiple language natural language processing and unavailability of people with appropriate data technology talent are some of the other challenges in implementing AI with banks (Kumar, K, 2020).
Conclusion
AI is going to be more powerful and smart in the future, to help any customer gain a stable banking experience. Artificial Intelligence (AI) has the potential to enhance corporate operations, provide custom designed services, and provide resources with broader goals such as economic inclusion (Celebrator, 2020). In banking enterprise, AI has been used in areas such as middle banking, operational performance, customer support, and analytics. Chatbots and robotics are widely used programs in Indian banking enterprise as well as gadget study algorithms have also been deployed in areas like KYC, fund transfer, fraud detection etc. AI cognition is growing on the scale of development as it reaches new heights in customer dating development through digitalization. It is expected that in future Indian banks are deploying brand-new technology along with gadget studies, block chains and analytics to overcome challenges like cyber threats, traditional banking methods, loss of schooling etc. Provide high-tech-enabled banking services.
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