TYPES OF AI AND THEIR IMPACT ON BANKS

What is Artificial Intelligence?

AI is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision making, and translation between languages.In computer science,artificial intelligence sometimes called machine intelligence,is demonstrated by machines,in contrast to the natural intelligence displayed by the humans and other animals.

Types of Artificial Intelligence

AI can be classified in any number of ways,there are two types of main classifications

Type 1

1.Weak AI or Narrow: It is focused on one narrow task, the phenomenon that machines which are not too intelligent to do their own work can be built in such a way that they seem smart.An example will be a ‘poker’ game where a machine beats human where in which all rules and moves are fed into the machines Here each possible scenario need to be entered beforehand manually.

2. Strong AI: The machine that can actually think and perform tasks on its host like a human being. There are no proper examples for this but some industry leaders are very keen on getting close to build a strong AI which has resulted in rapid progress

Type 2 (Based on Functionalities)

1. Reactive Machines: This is one of the basic forms of AI. It does not have the past memory and cannot use past information to the information for the future actions. Example: IBM chess program that beat Garry Kasparov in the 1990s.
2. Limited Memory: AI systems can beused past experience to inform future decisions.Some of the decision-making functions in self-driving cars have been designed in this way. Observations used to inform actions happening in the not so distant future, such a car that has changed lines.These observations are not stored permanently.
3. Theory of Mind: This type of AI should be able to understand people’s emotions, belief,thoughts, expectations,and be able to interact socially.Eventhough a lot of improvements are there in this field this kind of AI is not yet implemented.
4. Self-Awareness: An AI that has its own conscious,super intelligent, self-awarenessand sentient (In simple words a complete human being). Ofcourse, this kind of bot also does not exist and if achieved it will be one of the milestones in the field of AI

 

KEY COMPONENTS OF ARTIFICIAL INTLLIGENCE

Machine Learning (ML): It is a method where the target (goal) is defined and the steps to reach that target is learned by the machine itself by training (gaining experience). For example to identify a simple object such as an apple or orange. The target is achieved not by explicitly specifying the details about it and coding it but it is just as we teach a child by showing multiple different pictures of it and therefore allowing the machine to define the steps to identify it like an apple or an orange.

Natural Language Processing (NLP): Natural Language Processing is broadly defined as the automatic manipulation of natural language, like speech and text, by software. One of the well-known examples of this is email spam detection as we can see how it has improved in our mail system.

Vision: It can be said as a field which enables the machines to see. Machine vision captures and analyses visual information using a camera, analog-to-digital conversion, and digital signal processing. It can be compared to human eyesight but it is not bound by the human limitation which can enable it to see through walls (now that would be interesting if we can have implants that can make us see through the wall). It is usually achieved through machine learning to get the best possible results so we could say that these two fields are interlinked.

Robotics: It is a field of engineering focused on the design and manufacturing of robots. Robots are often used to perform tasks that are difficult for humans to perform or perform consistently. Examples include car assembly lines, in hospitals, office cleaner, serving foods, and preparing foods in hotels, patrolling farm areas and even as police officers. Recently machine learning has been used to achieve certain good results in building robots that interact socially.

Autonomous Vehicles: This area of AI has gathered a lot of attention. the list of vehicles includes cars, buses, trucks, trains, ships, submarines, and autopilot flying drones etc.

 

ARTIFICIAL INTELLIGENCE IN BANKING

Artificial Intelligence in banking is more than about chat bots. Here’s why banks, especially in India, should consider using the technology.

Banking business and technology leaders agree that Artificial Intelligence (AI) is among the key trends that will reshape thebanking industry.

AI technologies such as machine learning, deep learning, predictive/prescriptive analytics, virtual agents and natural language understanding technologies (e.g. Siri, Alexa, Google home) which were discussed above are gaining popularity among progressive banks. Financial Services is data intensive and therefore a great candidate for AI automation. AI technologies offers banks an opportunity to reinvent banking processes and gain unprecedented advantages.

Artificial Intelligence in Indian banking: Challenges and opportunities

Artificial Intelligence (AI) is fast evolving as the go-to technology for companies across the world to personalize experience for individuals. The technology itself is getting better and smarter day by day, allowing more and newer industries to adopt the AI for various applications. Banking sector is becoming one of the first adopters of AI. And just like other segments, banks are exploring and implementing the technology in various ways.

The rudimentary applications AI include bring smarter chat-bots for customer service, personalizing services for individuals, and even placing an AI robot for self-service at banks. Beyond these basic applications, banks can implement the technology for bringing in more efficiency to their back-office and even reduce fraud and security risks.

Unsurprisingly, research firms are bullish on the potential of AI in banking. According to Fintech India, the global spending in AI applications touched $5.1 billion, up from $4 billion in 2015. There is a keen interest in the Indian banking sector as well.

 

Advent of AI banking in India

According to recent report, 83% of Indian bankers believe that AI will work alongside humans in the next two years — a higher than the global average of 79%. “93% bankers in India said they increasingly use data to drive critical and automated decision-making. More partner-supplied customer data means a higher degree of responsibility for banks. Yet, 77% Indian bankers agree that most firms are not prepared to confront impending waves of corrupted insights from falsified data,” said the report.

AI is not new to India. Research institutions and universities have been working with various AI technologies for decades, and especially in the area of social transformation. With enabling technologies becoming a lot more accessible and inexpensive, AI is now becoming mainstream, with large enterprises and start-ups looking at different opportunities. Our research shows that the adoption of AI has the potential to add nearly $1 trillion to the Indian economy in 2035. AI adoption is still in its nascent stages, and a lot more needs to be done to realize its full potential

Application of AI and ML (machine learning) to different functions within the banking industry has enabled them to offer a far more personalized and efficient customer service. By achieving that, banks have also been able to gain better insights into their customers’ preference and expectations from the bank. Accordingly, automation of back-end workflows has shown better outcomes. According to various industry reports, more than 36% of large financial institutions are already investing in such technologies, and close to 70% are planning to in the near future.

 

AI IS NOT JUST CUSTOMER SERVICE IN BANKS

State Bank of India, the largest bank in India, last year conducted “Code for Bank” hackathon to encourage developers to build solutions leveraging futuristic technologies such as AI and Blockchain into the banking sector.SBI hasdeveloped chatbot based on AI ,SIA (Strategic Intelligent Assistant ) to give the information to 850 million queries on various topics and products viz., Savings Bank, Pre approved personal loans ,Credit cards, Insurance etc. Private banks like HDFC Bank and ICICI Bank have already introduced chat-bots for customer service. Some have even gone ahead with placing robots for customer service. Last year, Canara Bank installed Mirta and Candirobots at some of its offices.

Payment companies are using AI to offer personalized payment experience to consumers. By applying AI and analyzing past payment patterns, payment systems can prompt the preferred payment instrument which best suits a purchase at the time of checkout. Say a consumer avails EMI option frequently for his big-ticket purchases, then the best EMI option is made available to the consumer at the time of checkout. Such personalized consumer experiences drive up consumer spending and creates stickiness to the product consumers are using.

COMMON USES OF AI IN BANKING IN FUTURE

Compliance,Fraud Detection and Anti -money- laundering: Anomaly detection can be used to increase the accuracy of credit card fraud detection and anti-money laundering.Avoiding fraud and money laundering is a challenge for many financial organizations.AI has the potential to help banks become more efficient in the process of detecting fraud and money laundering. To quickly identify potential fraud, A I engineers have developed tools and systems that automatically conduct and compress data that normally requires many hours of labor in just matter of minutes .Larger institutions are more inclined to update their legacy systems due to the rising number of fintech companies that are adopting AI. One of the Banking Giants, Citibank, is already using machine learning and Bigdata to prevent criminal activitiesand monitor potential threats to customers in commerce. The company has adopted a new anti-money laundering structure and has invested over $11 million to launch a new personal finance app that encourages customers to participate in third party services.

Process Automation.

Process Automation is one of the key drivers of automation in banks and financial institutions, but it’s also evolving into cognitive process automation., where AI systems are able to perform more complex automation .JP Morgan Chase recently invested in a new technology called COiN that reviews the documents and extracts data in much less time than it would take a human. This tool reviews about 12,000 documents (which without automation would require more than 360,000 hours of work) in just seconds

Customer Support and Helpdesk: Humanoid Chatbot interfaces can be used to increase efficiency and reduce cost for customer interactions. (e.g. SIA of SBI)

Risk Management: Tailored products can be offered to clients by looking at historical data, doing risk analysis, and eliminating human errors from hand-crafted models.

Security: Suspicious behavior, logs analysis, and spurious emails can be tracked down to prevent and possibly predict security breaches.

Digitization and automation in back-office processing: Capturing documents data using OCR and then using machine learning/AI to generate insights from the text data can greatly cut down back-office processing times.

Wealth management for masses:Personalized portfolios can be managed by Bot Advisors for clients by taking into account lifestyle, appetite for risk, expected returns on investment, etc.

ATMs: Image/face recognition using real-time camera images and advanced AI techniques such as deep learning can be used at ATMs to detect and prevent frauds/crimes.

 

AI is not without challenges

A wide implementation of a high-end technology like AI in India is not going to be without challenges. From the lack of a credible and quality data to India’s diverse language set, experts believe a number of challenges exist for the Indian banking sector using AI.

A key challenge is the availability of the right data. Data is the lifeblood of AI, and any vulnerability arising from unverified information is a serious concern for businesses. Imagine for example, the risks that could arise from KYC compliance AI systems if the data sources are incorrect. Or consider the efficacy of a fraud detection AI system without the right kind of data. Structured mechanisms for collecting, validating, standardizing, correlating, archiving and distributing AI relevant data is crucial.

India has 150+ languages with sizable spoken population. Applications which use speech to text or text to speech rely on natural language processing (NLP) libraries and techniques. Banks can use the existing technologies to start with to support some major Indian languages, but in order to effectively reach out to wider population in India, much more progress is required on NLP front.

Data access and data privacy is a central aspect of any AI work banks do. These aspects will be of paramount importance with introduction of regulations in Europe such as GDPR (General Data Protection Regulation). GDPR regulation is currently applicable to European citizens, but India and other countries have their own data privacy regulations. Banks in India will have to build AI systems with GDPR and similar privacy regulations in mind.

Experts also have also stressed the need for more skilled engineers to drive the segment.

The biggest challenge is the scarcity of trained human resources; the existing workforce is not familiar with latest tools and applications. Secondly, the AI technology is a big threat to redundant employees in the banking sector. The mass adoption of AI may cause a grave unemployment problem in the sector

One of the important challenges that is faced by Industry and not just banks in India is unavailability of people with right data science skills. With only small number of good data scientists available to do AI work, the industry needs to work with universities in India to develop skilled data scientists as well as develop in-house training programs to train employees on data science skills.Also identification of right use cases for AI implementation with the help of domain experts and data scientists can help banks in successful implementation of AI technologies for banking functions

CONCLUSION.

AI can provide quick and personalized services by dealing with each customer and focusing on their specific requirements. It can be used to collect information automatically, build models on that information, inference and communicate in natural way.

The use of Robotics which is part of AI , in the Indian Banking sector though not yet wide spread, is expected to gain ground in  the coming years.Robotics is  expected to automate processes which are repetitive in nature ,rule based, and requires less human judgement.Apart from humanoid robots providing  customer service, software robots are also deployed in functions such as retail banking operations,agri-business,trade & forex,treasury and human resources management to name a few..

The entire eco system of Banking comprising customers, employees, topmanagement, competitors is undergoing a transformation over a period.Banks now started focusing on building center of excellence in innovation and design. AI is one of the innovations that can help the banks in improving in every walk of business and in achieving the business targets and organizational goals in effective and efficient manner with accuracy and less human interference.

 

Authored By:

N. Gopala Krishna Murthy
Chief Manger & Faculty
State Bank Institute of –
Innovation and Technology
(Sbiit), Hyderabad