- Artificial Intelligence is the Intelligence demonstrated by machines, unlike the natural Intelligence displayed by human and animals, which involve consciousness and emotionality. Swedish philosopher Nick Bostrom, in his book Super Intelligence, says, “Machine learning is the last invention for mankind.” From electronic trading platforms to medical diagnostics, robot control, entertainment, education, health and commerce, Artificial Intelligence services and digital disruption have hit every sector in the 21st century. AI has made its presence felt across all sectors due to its ability to help consumer innovation. This enables users to make faster and more informed decisions with increased efficiency.
Of late, the banking sector is becoming an active adapter of artificial intelligence — exploring and implementing this technology in new ways. The entry of artificial intelligence into the banking sector was not recognized and slowed down until the era of Internet banking.
1. Artificial narrow intelligence (ANI) :- It has a narrow range of ability, it is basically goal oriented.
2.Strong AI is named as AGI :- Artificial General Intelligence (Hypothetical Ability of an intelligent agent.at par with human capability
- Natural AI is named as ABI :- Artificial Biological Intelligence or Artificial super Intelligence (Property of a machine to understand or self aware and surpass the cognitive ability, memory, learning and decision making, it is more capable than human.
- One of the first steps was introducing Ally Bank (USA) -Allie Assistant in 2015, which can respond to voice and text, make payments on behalf of the customer, summarize the account, monitor savings, spend systems and use natural language processing to understand and resolve customer queries.
- Banks around the world have adopted versions of their best chatbots: from Erica to iPal, to Eva and most popular — SBI’s SIA. According to Pazzo (a start-up that developed the SIA), SIA can handle up to 10,000 inquiries per second and is one of the world’s largest deployments of artificial intelligence in consumer-facing banking. In this era of technological revolution, the banking sector has witnessed a paradigm shift in its policy from brick-and-mortar to digital banks. Banks are increasingly spending on Machine Learning in Artificial Intelligence and Data Analytics for personalized and fast customer experience to gain tech-savvy and millennial class benefits.
- According to the PWC Fintech Trends India report , global spending on artificial intelligence hit $ 5.1 billion. IHS Markit’s “Artificial Intelligence in Banking ” report predicts that spending will rise to $ 41.1 billion and reach $ 300 billion by 2030. It shows that artificial intelligence has reached the point where it becomes affordable and efficient for implementation in financial services. The challenge now is to explore more ways to harness the powers of artificial intelligence to streamline internal banking processes and improve customer experience.
- Front-end operations of artificial intelligence involve direct interaction with clients. It includes applications and payment interfaces, digital wallets, chatbots or interactive voice response systems. To secure the system, analyse fraudulent transactions and generate reports, systematic processing of large chunks or terabytes of data to improve compliance makes back-end operations more complex. We will now discuss the future of artificial intelligence in each of these fields.
- Features of Artificial Intelligence
- Improved customer experience
- Being a key driver of the customer service industry, customer service is at the forefront of any business. AI can be used to gain a better understanding of customer spending patterns, which helps banks to customize products by adding personalized features. It provides meaningful customer engagement, building strong relationships and business growth for the bank.
- For example, SBI is working on the Automated Real-Time Customer Emotion Feedback Exchange (ARTCEF) system using AI to analyse customers’ facial expressions in real-time. AI can also be used to provide a personalized payment experience, such as the most suitable EMIs at checkout based on past payment methods, often offering multiple currency cards to customers going abroad. Biometric face recognition without AI helps set up ATMs. The card’s purpose is to use real-time camera images and detect and prevent fraud at the same time.
- Use of chatbots
- Chatbots and interactive voice response systems that utilize natural language processing are increasingly used by banks these days to increase service efficiency. This reduces the cost of human capital, resulting in savings to the bank. Customer satisfaction also increases because they are able to get their homes comfortably without having to visit the branches — saving them time. Chatbots can be improved in the future to announce new offers to a customer, such as loans, or alert customers if they have any EMI payments, suggesting good discounts based on the bank’s relationship with e-commerce sites.
- Data Analytics to predict future results and trends:
- The effortless and fast processing of large volumes of data enables banks to observe patterns of customer behaviour, predict future results, and contact the right customer with the right product at the right time. It also helps in detecting fraud and fraudulent transactions and simultaneously identifying the anti-moneylending model on a real-time basis.
- Machine learning and cognition serve to identify suspicious data patterns and convince banks if the original source of money is legal or illegal. AI can also study the behaviour of past customers to predict future needs, which can help banks to market and sell successfully.
- Wealth management and portfolio management
- AI-based systems help potential investors by analyzing their salary and spending policies. They can also assess market trends and choose the right funds for their portfolio by determining the appropriate amount to invest each month to realize their dreams. All this can be done without visiting branches or hiring experts. In the world of banking telephone at your fingertips, mutual funds and fixed deposits can be created at home and money can be returned when needed.
- AI can be leveraged to instantly inform customers of any suspicious transactions beyond their usual patterns.
- Focus on better operations, effective cost management vs. profitability:
- Banks must make a profit in order to survive, and today, banks face considerable pressure on their margins. Regulators and their continued focus on transparency make many businesses profitable.
- AI technologies enable banks to bring more efficiency to their operations and manage costs. Robotic Process Automation (RPA) and Intelligent Process Automation (IPA) are very helpful here. Applying financial contracts is only a matter of seconds, thanks to AI. They also help to manage contracts and act as brokers, taking on common tasks simultaneously, thereby improving productivity and efficiency. All this will lead to increased revenues, reduced costs and an increase in profits. Robotic automation of processes can rebuild the financial sector and make it more humane and smarter. Approximately 80% of the repetitive work processes help the authorities to devote their time to value-added activities that require high levels of human intervention such as automation and product marketing.
- What we need now is not just the empowerment of the banks through automation, but the intelligent transformation of the entire system until they can defeat the newly emerging fintech players. This has prompted many banks to use software robotics to simplify the back-end process and achieve a better functional design. SBI plans to set up an Innovation Center link to explore RPA, which will help in making internal banking processes more efficient.
- Intelligent Character Recognition System
- This system has been identified by some foreign banks, used to collect important information from old loan applications, to lease contracts and to feed a central database that is accessible to everyone. This helps with expensive and error-prone banking services, such as managing claims, drastically reducing the time spent reading or recording client information.
- For example, JPMorgan Chase’s COIN documents and extracts data from 12,000 documents (which require more than 360,000 hours of work without automation) in just seconds.
- The idea of credit to a minus percentage of the Indian population. To date, applying for a loan is considered an awkward process. Analyzing the creditworthiness of a person due to a lack of credit history is annoying for banks.
- The use of Big Data and Machine Learning to analyze customer’s cost patterns and behavioral data over 10,000+ data points helps the bank to have insight into the customer’s creditworthiness. It helps in giving pre-approved loans to a large range of customers without the need for paperwork and allows the self-employed and students (because they lack the financial fold) to get credit. In the case of SME and corporate lending, AI simplifies the complex and complex borrowing process, analyzing market trends and identifying potential risks in lending, future behavior and even the slightest likelihood of fraud.
- Risk management and fraud detection
- The Punjab National Bank scandal has put the banking sector at enormous risk, shaking up regulators, financial and stock markets and the banking industry. AI and appropriate due diligence can monitor such potential threats and help banks to install fool-proof surveillance and fraud detection systems. Surveillance in banks was done through audits and sampling. Some data sets and files that cause major losses are not covered in these models. The algorithmic rules-based approach helps monitor each file, and machine learning techniques can keep a database of such files vulnerable to the bank.
- Banks can use AI to detect fraud in transactions or to detect any suspicious activity in a customer’s account based on behavioral analysis while providing safe and fast transactions. With increasing cybercrime in recent years, AI can be used to manage cyber-security and, most importantly, protect personal data. Citibank has already invested over $ 11 million in new money laundering, using machine learning and big data.
- AI-based systems help in compliance by ensuring the functionality of internal control systems. AI is also a game-changer by detecting insider trading that leads to market abuse.
- Insurance Underwriting and Claims:
- In this era of bankruptcy, consumers are more likely to arrive at banks than visit insurance companies. The insurance industry can take advantage of AI in detecting underwriting, claim-handling policies and fraud. This helps identify risky behaviour and charges higher premiums to groups of customers. There is an enormous amount of data in insurance companies that can help you create mathematical models and accurately predict risky behaviours. Banks can also provide such data for use in customer risk identification. This reduces turn-around-time (TAT) for both loans and insurance. For example, in order to analyse the damage to the vehicle, deep learning techniques can analyse the image of the vehicle and calculate the cost of repair using the models attending.
- Threats by AI
- Alibaba founder Jack Ma warned viewers at the World Economic Forum 2018 in Davos that AI and big data are a threat to humans and will stop people from empowering them. The massive expansion of AI in banks comes with its share of risks and opportunities. Banks increase their investments in AI every year, often with obsolete risk. We need to understand the risks that the AI can also face.
- Loss of jobs
- Automation of Tasks Banks faces the risk of backlash from their employees, leading to job loss and job restructuring. AI, in a way that maximizes organizational productivity, redefines the way employees do their jobs. This can lead to dissatisfaction among employees, resulting in resignations or layoffs. AI can also replace a teller, customer service executive, loan processing officer, compliance officer, and finance manager.
- Process Opacity:
- Although deep learning models and neural networks in AI have proven to be more complete than human decision-making over time, they are often not transparent in terms of how such conclusions are made. Explaining it to regulators can be a challenge for bankers. The Justice Srikrishna Committee said that the biggest challenge in using big data and artificial intelligence is to work outside the framework of traditional privacy principles. It now operates in the reverse way and risks banks unknowingly. This leads to hidden bias in decision making as AI has access to all users ’data.
- Reduced Customer Loyalty
- There is also a fear that a lack of customer contact and a lack of the essence of “human touch” will diminish customer loyalty. Because banks, especially in India, help so many people in fulfilling their long-term dreams, they have emotional value — be it a beautiful home or a good education for students. All of this could be lost due to AI and automation. Socio-economically disadvantaged groups are the biggest losers and are most affected by such a low level of education and the digital divide.
- Future Action
- Tech columnist Nick Bilton writes in the New York Times, “[Insurgents of artificial intelligence] grow quickly and become scary and catastrophic. Imagine that the medical robot, which was originally programmed to eliminate cancer, was able to conclude that the best way to eradicate cancer is to eradicate infected humans. “The message here is that banks need to raise awareness of the impacts of banks. Develop digitization and broad foresight in the possibilities of AI — so that we as humans have control over AI and not the reverse. One area that banks should focus on now is data acquisition. Lack of proper customer records is the biggest obstacle to AI.
- We need to make sure that the data used by banks are KYC compliant clean data as it is used in AI models. Huge data infrastructure is needed to impact AI. Verification of the correctness and accuracy of data is also required before using such technology in the public domain.
- Analysis and authentication of data:
- The amount of data with the banks is very large, while Oracle and Accenture store all the data in the bank. What we need is a proper analysis of the data, and it requires a high level of leadership expertise to bring cross-functional teams together — one with knowledge of financial business, and the other with different disciplines for the effective use of such data sets with the required machine learning skills to create a framework and infrastructure. AI remains a niche-based domain with a shortage of talent and expertise.
- Leakage and data misuse
- S. And the U.K. Many experts in cybersecurity believe that cyber, political and physical threats arise with the capabilities and reach of AI. The recent Facebook scandal highlights the risks that corrupt data practices can bring to a company. We also need to ensure complete transparency when entering new AI projects so that banks do not face reputation risks.
- Banks should start building AI SYSTEMS with small complex data and add the latter, thus creating a universal record of each client. Adequate investment should be made in storing data securely and preventing leakage. This helps the bank to identify potential risks during the project implementation phase and to effectively identify and then implement the company’s goals and priorities. Artificial intelligence will soon become the sole determinant of banks’ competitive position and is the key to maximizing their competitive advantage.
Artificial Intelligence (AI) is a science and a set of computational technologies that are inspired by—but typically operate quite differently from—the ways people use their nervous systems and bodies to sense, learn, reason, and take action. While the rate of progress in AI has been patchy and unpredictable, there have been significant advances since the field’s inception sixty years ago. Once a mostly academic area of study, twenty-first century AI enables a constellation of mainstream technologies that are having a substantial impact on everyday lives. Computer vision and AI planning, for example, drive the video games that are now a bigger entertainment industry than Hollywood. Deep learning, a form of machine learning based on layered representations of variables referred to as neural networks, has made speech-understanding practical on our phones and in our kitchens, and its algorithms can be applied widely to an array of applications that rely on pattern recognition. Natural Language Processing (NLP) and knowledge representation and reasoning have enabled a machine to beat the Jeopardy champion and are bringing new power to Web searches.
While impressive, these technologies are highly tailored to particular tasks. Each application typically requires years of specialized research and careful, unique construction. In similarly targeted applications, substantial increases in the future uses of AI technologies, including more self-driving cars, healthcare diagnostics and targeted treatments, and physical assistance for elder care can be expected. AI and robotics will also be applied across the globe in industries struggling to attract younger workers, such as agriculture, food processing, fulfillment centers, and factories. They will facilitate delivery of online purchases through flying drones, self-driving trucks, or robots that can get up the stairs to the front door.
This report is the first in a series to be issued at regular intervals as a part of the One Hundred Year Study on Artificial Intelligence (AI100). Starting from a charge given by the AI100 Standing Committee to consider the likely influences of AI in a typical North American city by the year 2030, the 2015 Study Panel, comprising experts in AI and other relevant areas focused their attention on eight domains they considered most salient: transportation; service robots; healthcare; education; low-resource communities; public safety and security; employment and workplace; and entertainment. In each of these domains, the report both reflects on progress in the past fifteen years and anticipates developments in the coming fifteen years. Though drawing from a common source of research, each domain reflects different AI influences and challenges, such as the difficulty of creating safe and reliable hardware (transportation and service robots), the difficulty of smoothly interacting with human experts (healthcare and education), the challenge of gaining public trust (low-resource communities and public safety and security), the challenge of overcoming fears of marginalizing humans (employment and workplace), and the social and societal risk of diminishing interpersonal interactions (entertainment). The report begins with a reflection on what constitutes Artificial Intelligence, and concludes with recommendations concerning AI-related policy. These recommendations include accruing technical expertise about AI in government and devoting more resources—and removing impediments—to research on the fairness, security, privacy, and societal impacts of AI systems.
Contrary to the more fantastic predictions for AI in the popular press, the Study Panel found no cause for concern that AI is an imminent threat to humankind. No machines with self-sustaining long-term goals and intent have been developed, nor are they likely to be developed in the near future. Instead, increasingly useful applications of AI, with potentially profound positive impacts on our society and economy are likely to emerge between now and 2030, the period this report considers. At the same time, many of these developments will spur disruptions in how human labor is augmented or replaced by AI, creating new challenges for the economy and society more broadly. Application design and policy decisions made in the near term are likely to have long-lasting influences on the nature and directions of such developments, making it important for AI researchers, developers, social scientists, and policymakers to balance the imperative to innovate with mechanisms to ensure that AI’s economic and social benefits are broadly shared across society. If society approaches these technologies primarily with fear and suspicion, missteps that slow AI’s development or drive it underground will result, impeding important work on ensuring the safety and reliability of AI technologies. On the other hand, if society approaches AI with a more open mind, the technologies emerging from the field could profoundly transform society for the better in the coming decades.