Banking Article, Banking Finance 2021, Banking Finance December 2021

Artificial Intelligence

Artificial intelligence (AI, also machine intelligence, MI) is intelligence demonstrated by machines, in contrast to the natural intelligence (NI) displayed by humans and other animals. In natural intelligence emotions, senses and circumstances operate in tandem by instinct. But in AI where no instinct is available, all logical outcomes are to be forecasted and synthesised to the device. It is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. In other words, it is an endeavour to replicate or simulate human intelligence in machines.

Some of the activities computers with artificial intelligence are designed for include:

  • Speech recognition
  • Learning
  • Planning
  • Problem solving

Research associated with artificial intelligence is highly technical and specialized. The core problems of artificial intelligence include programming computers for certain traits such as Knowledge, Reasoning, Problem solving, Perception, Learning, Planning and Ability to manipulate and move objects. Patrick Winston, the Ford professor of artificial intelligence and computer science at MIT, defines AI as ”algorithms enabled by constraints, exposed by representations that support models targeted at loops that tie thinking, perception and action together.”

Knowledge engineering is a core part of AI research. Machines can often act and react like humans only if they have abundant information relating to the world. Artificial intelligence must have access to objects, categories, properties and relations between all of them to implement knowledge engineering. Initiating common sense, reasoning and problem-solving power in machines is a difficult and tedious task. Robotics is also a major field related to AI. Robots require intelligence to handle tasks such as object manipulation and navigation, along with sub-problems of localization, motion planning and mapping.

Machine learning is another application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

Examples of AI applications:

  1. Voice recognition Systems: Voice recognition systems such as Apple’s Siri, Microsoft’s Cortana use machine learning and deep neural networks to imitate human interaction. As they progress, these apps will learn to ‘understand’ the nuances and semantics of our language. For example, Siri can identify the trigger phrase ‘Hey Siri’ under almost any condition through the use of probability distributions. By selecting appropriate speech segments from a recorded database, the software can then choose responses that closely resemble real-life conversation. Amazon’s Alexa and Echo and Google’s Google assistant are also examples of voice recognition systems.
  2. Facebook: Remember when Facebook used to prompt you to tag your friends? Nowadays, the social network’s algorithms recognise familiar faces from your contact list, using some seriously impressive technology. ‘We closely approach human performance,’ says Yaniv Taigman, one of the masterminds behind DeepFace, Facebook’s machine learning facial recognition software.
  3. Google Maps:  Google introduced machine learning to Google Maps in 2017, improving the usability of the service. These deep learning algorithms help the app extract street names and house numbers from photos taken by Street View cars and increase the accuracy of search results.
  4. Paypal: PayPaluses machine learning algorithms to detect and combat fraud. By implementing deep learning techniques, PayPal can analyse vast quantities of customer data and evaluate risk in a far more efficient manner. Traditionally, fraud detection algorithms have dealt with very linear results: fraud either has or hasn’t occurred. But with machine learning and neural networks, PayPal is able to draw upon financial, machine, and network information to provide a deeper understanding of a customer’s activity and motives.
  5. Netflix: More than 80 percent of TV shows on Netflix are found through its recommendation engine. Machine learning is integral to this process, as the platform caters to more than 100 million subscribers. While the finer details of Netflix’s machine learning algorithms are kept behind closed doors, Tod Yellin, the company’s VP of product innovation states there are two things that feed the neural network: user behaviour and programme content. Together, these datasets create multiple ‘taste groups’, which tell the recommendation engine which programmes to serve up.
  6. Chatbots: Chatbots are artificial intelligence based automated chat systems which simulate human chats without any human interventions. They work by identifying the context and emotions in the text chat by the human end user and respond to them with the most appropriate reply. With time, these chat bots collect massive amount of data for the behaviour and habits of the user and learns the behaviour of user which helps to adapt to the needs and moods of the end user.
  7. Other Applications:
  • Disease mapping and prediction tools
  • Manufacturing and drone robots
  • Optimized, personalized healthcare treatment recommendations
  • Conversational bots for marketing and customer service
  • Robo-advisors for stock trading
  • Spam filters on email

Narrow Artificial Intelligence

Narrow AI is all around us and is easily the most successful realization of artificial intelligence to date. With its focus on performing specific tasks, Narrow AI has experienced numerous breakthroughs in the last decade.A few examples of Narrow AI are Google search, Image recognition software, Siri, Alexa and other personal assistants, Self-driving cars and IBM’s Watson.


Artificial Intelligence and its relevance to Banking

Banks have the history of adapting the latest technology innovations to redefine the banking outlook and to see how customers are reacting to it. First computers are introduced into banks and then in 1960s ATMs, electronic, card-based payments in the ’70s. The 2000s saw broad adoption of 24/7 online banking, followed by the spread of mobile-based “banking on the go” in the 2010s.

In recent years, if Artificial Intelligence has impacted one industry more than any other, it’s the Banking industry. For organizations working in the banking industry, it has become increasingly crucial to keep up with competition, and increase their standing as an innovative company

Here are five key applications of artificial intelligence in the Banking industry that will revolutionize the industry in the next 5 years.

  1. AML Pattern Detection: Anti-money laundering (AML) refers to a set of procedures, laws or regulations designed to stop the practice of generating income through illegal actions. In most cases, money launderers hide their actions through a series of steps that make it look like money that came from illegal or unethical sources are earned legitimately. Most of the major banks across the globe are shifting from rule based software systems to artificial intelligence based systems which are more robust and intelligent to the anti-money laundering patterns.
  2. Chat bots are already being extensively used in the banking industry to revolutionize the customer relationship management at personal level. Bank of America plans to provide customers with a virtual assistant named “Erica” who will use artificial intelligence to make suggestions over mobile phones for improving their financial affairs. Allo, released by Google is another generic realization of chat bots. Chatbots of 4 leading Indian Banks are, SBI’s SIA, HDFC’s EVA, ICICI’s iPal and that of Axis Bank is Aha. Credit card issuer SBI Card has announced the launch of the Electronic Live Assistant or ELA, a virtual assistant for customer support and services. Driven by Artificial Intelligence and Machine Learning algorithms, ELA will revolutionise the way customers interact with the company. ELA is designed to enhance the customer experience by providing relevant and instant responses to customer queries. The virtual assistant is currently hosted on the SBI Card website and will soon be integrated on the mobile app as well.
  3. Algorithmic trading: Plenty of Hedge funds across the globe are using high end systems to deploy artificial intelligence models which learn by taking input from several sources of variation in financial markets and sentiments about the entity to make investment decisions on the fly. Reports claim that more than 70% of the trading today is actually carried out by automated artificial intelligence systems.
  4. Fraud detection: Fraud detection is one of the fields which has received massive boost in providing accurate and superior results with the intervention of artificial intelligence. It’s one of the key areas in banking sector where artificial intelligence systems have excelled the most. Starting from the early example of successful implementation of data analysis techniques in the banking industry is the FICO Falcon fraud assessment system, which is based on a neural network shell to deployment of sophisticated deep learning based artificial intelligence systems today, fraud detection has come a long way and is expected to further grow in coming years.
  5. Customer recommendations: Recommendation engines are a key contribution of artificial intelligence in banking sector. It is based on using the data from the past about users and/ or various offerings from a bank like credit card plans, investment strategies, funds, etc. to make the most appropriate recommendation to the user based on their preferences and the users’ history. Recommendation engines have been very successful and a key component in revenue growth accomplished by major banks in recent times.
  6. Digitisation of processes: Digitisation of KYC processes eliminate the need for physical document submission and verification. AI based computer vision technology can be used to verify documents, Optical/Intelligent Character recognition (OCR/ICR) technologies to digitise scanned documents, and Natural Language Processing (NLP) to make sense of them.
  7. Decision Making: AI can be of great use in areas where decisions are based on available structured and unstructured data. For example, it can predict potential loan defaulters and offer risk mitigation strategies too. Another use is AI determine the best time to approach a customer to sell a new product. AI based smart environments can collate data from multiple sources and drive an inference and enable SMEs to take decisions. Also can improve straight-through using intelligent Automation to automate repetitive processes that need decision making.
  8. 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.
  9. Risk management and security: specially designed products can be given to clients by looking at historical data, doing risk analysis and eliminating human errors from man-made products. Suspicious behaviour logs analysis and spurious emails can be tracked to prevent security breaches.
  10. Roborts in banking:Artificial Intelligence humanoid applications (Roborts) can be used for customer interactions at branches that reduces cost and increases efficiency. “Lakshmi”, India’s first Robot launched by City Union Bank can interact with customers.The other robots in Indian Banking industry are, IRA of HDFC Bank, Mitra and Candy of Canara Bank. ICICI bank has deployed Industrial ‘robotic Arms’ for note sorting at its currency chests.Pepper isprobably the most popular robot in the banking industry. The robot is able to recognise human emotions and adjust to it. Pepper’s developer SoftBank uses this robot in 140 stores in Japan. Pepper also works in restaurants, hotels, hospitals and stores all around the world.

Artificial intelligence has transformed every aspect of the banking process. AI technologies are making banking processes faster, money transfers safer and back-end operations more efficient.AI has impacted every banking “office” — front, middle and back. That means even if you know nothing about the way your financial institution uses, say, complex machine learning to fend off money launderers or sift through mountains of data for fraud-related anomalies, you’ve probably at least interacted with its customer service chatbot, which runs on AI.

Application of AI in military Operations:

The advent of AI could fundamentally change the character of warfare, resulting in a transformation from todays “informatized” ways of warfare to future “intelligentized” warfare, in which AI will be critical to military power. AI can enhance future capabilities in following areas:-

  1. Intelligent and autonomous unmanned systems.
  2. AI-enabled data analysis, information processing and intelligence analysis.
  3. War-gaming, simulation, and training.
  4. Defence, offence and command information warfare.
  5. Intelligent support to command decision making.

Benefits of Artificial Intelligence in Medical field:

You know technology really day by day reaches the zenith and brought new things in front of us for convenience. Today’s artificial intelligence technology allow non-living things like robots & computers to think for themselves to an extent. Take a look at artificial intelligence benefits in the healthcare industry.

  • Fast & Accurate diagnostics: Some diseases require immediate action otherwise they will become more severe. In the case of AI, the neural network of the brain is look alike, has the ability to learn from previous cases. After some studies or research on artificial neural networks, researchers says that it is scientifically proven that these networks can diagnose fast & accurate some other diseases includes eye problems, malignant melanoma etc.
  • Reduce Human errors: Profession of doctor is very sensitive, they have to take care of each & every patient. In a day they can see a lot of patients which can be very exhausting because it requires attention and knowledge of the patient. Sometimes due to lack of activeness, human error may threaten the patient safety. To overcome this AI as a super human spell checker will assist doctors by eliminating human error & relieve them of monotonous & time-consuming tasks.
  • Cost Reduction: With the emerging technologies including artificial intelligence, the patient can get doctor assistance without visiting hospitals/clinics which results in cost cutting. AI assistants provide online care & assist patients to add their data more frequently via online medical records etc.
  • Virtual Presence: This technology also known as Telemedicine which allows specialists to assist their patients who live at remote locations. Using a remote presence robot, doctors can engage with their staff & patients in hospitals/clinics & assist or clear their queries.

Finally, it is evident that AI is here to stay, and is impacting a large number of industries, Banking is an early adopter of this trend.  This trend is likely to grow exponentially in the future. Companies that embrace this trend are likely to be winners over the next decade.

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