Artificial Intelligence And Intelligent Banking
BANKING FINANCE|October 2021
Is artificial intelligence less than our intelligence? - Spike Jonze
Biswajit Sen

Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.

The ideal characteristic of artificial intelligence is its ability to rationalize and take actions that have the best chance of achieving a specific goal. It was in the mid-1950s that John McCarthy, widely recognized as the father of Artificial Intelligence, coined the term Artificial Intelligence which he would define as the science and engineering of making intelligent machines.

Researchers, scientists, engineers, linguists, domain experts are working continuously and passionately to evolve the A.I. As technology advances, previous benchmarks that defined artificial intelligence become outdated. For example, machines that calculate basic functions or recognize text through optical character recognition are no longer considered to embody artificial intelligence.

AI is continuously evolving to benefit many different industries. Machines are wired using a cross-disciplinary approach based on mathematics, computer science, linguistics, psychology, health care, sales retails, operations, e-commerce and more. Coming to financial industry, where it is used for fraud detection, trade and sales forecasting ease of operation, voice-assisted banking.

Artificial Intelligence (AI) Overview

A thorough and hype-free review of AI in business was published recently by Deloitte, Demystifying Artificial Intelligence, suggesting the term cognitive technologies to encourage focus on the specific, useful technologies that emerge from the broad field of AI.

However labelled, the field has many branches, with many significant connections and commonalities among them. The most active today are shown here:

Analytics is subset of A.I. which falls under supervised learning in machine learning segment. Analytics is the systematic computational analysis of data or statistics. It is used for the discovery, interpretation and communication of meaningful patterns in data. It also entails applying data patterns towards effective decision making.

It can be valuable in areas rich with recorded information; analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance.

Organizations may apply analytics to business data to describe, predict, and improve business performance. Specifically, areas within analytics include predictive analytics, prescriptive analytics, enterprise decision management, descriptive analytics, cognitive analytics, Big Data Analytics, retail analytics, supply chain analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modelling, web analytics, call analytics, speech analytics, sales force sizing and optimization, price and promotion modelling, predictive science, graph analytics, credit risk analysis, and fraud analytics.

Since analytics can require extensive computation (see big data), the algorithms and software used for analytics harness the most current methods in computer science, statistics, and mathematics.

Difference between Analytics, AI, NLP, ML, NN and DL

AI or Artificial Intelligence: Building systems that can do intelligent things.

NLP or Natural Language Processing: Building systems that can understand language. It is a subset of Artificial Intelligence.

ML or Machine Learning: Building systems that can learn from experience. It is also a subset of Artificial Intelligence.

NN or Neural Network: Biologically inspired network of Artificial Neurons

DL or Deep Learning: Building Systems that use Deep Neural Network on a large set of data It is a subset of Machine Learning.

Analytics or Data science: The systematic computational analysis of data or statistics

Types of Artificial Intelligence (AI) There are 3 types of artificial intelligence (AI):

Narrow or weak AI,

General or strong AI

Artificial superintelligence

ANI - Artificial Narrow Intelligence: It has a narrow range of abilities. It comprises of basic/role tasks such as those performed by chatbots, personal assistants like SIRI by Apple, Cortana by Microsoft, IBM's Watson, Image / facial recognition software, Disease mapping and prediction tools, Manufacturing and drone robots, Email spam filters / social media monitoring tools for dangerous content, Entertainment or marketing content recommendations based on watch/listen/purchase behaviour.

AGI - Artificial General Intelligence: It is on par with human capabilities. Artificial General Intelligence comprises of human-level tasks such as performed by self-driving cars by Uber, Autopilot by Tesla. Fujitsu-built K, one of the fastest supercomputers, is one of the most notable attempts at achieving strong AI, but considering it took 40 minutes to simulate a single second of neural activity, it involves continual learning by the machines.

ASI - Artificial Super Intelligence: It is more capable than a human. Artificial Super Intelligence refers to intelligence way smarter than humans. ASI would have a greater memory and a faster ability to process and analyse data and stimuli. Consequently, the decision-making and problem-solving capabilities of super-intelligent beings would be far superior to those of human beings.

Stages of Artificial Intelligence (AI)

Stage 1 - Machine Learning It is a set of algorithms used by intelligent systems to learn from experience.

Stage 2 - Machine Intelligence These are the advanced set of algorithms used by machines to learn from experience. eg - Deep Neural Networks. Artificial Intelligence technology is currently at this stage

Stage 3 - Machine Consciousness It is self-learning from experience without the need of external data.

Artificial Intelligence (AI) industry in India - The current status

According to a source, around 500 start-ups and businesses are using AI domains. Most of the growth in AI in India can be seen in the private sector. The government sector in the NITI Aayog plan developed a National level Strategy for bringing Artificial Intelligence in India. Even though the private sector has the major share of AI services in the industry but the government sector is still the largest customer for data science in terms of the Indian economy.

There are several start-ups that are based in cities such as Bengaluru, New Delhi, Mumbai and Hyderabad which work on artificial intelligence principles to serve consumers better. Their product range varies from multi-lingual Chatbots to online shopping assistance and automated consumer data analysis. The companies have been working in areas such as e-commerce, healthcare, edtech, fintech etc. Though in their nascent stage, the performance of these companies have been promising.

India is third in terms of investment, just lags behind U.S.A. and China. With a copious pool of STEM talent and with growing population of youngsters, India will be banking on AI for its economic growth and improvement in quality of life of its citizens.

The challenges Facing India's Artificial Intelligence (AI) Development

1. AI-based applications to date have been driven largely by the private sector and have been focused primarily in consumer goods. The emergent scale and implications of the technology make it imperative for policymakers in government to take notice.

2. Early lessons of AI success in the United States, China, South Korea, and elsewhere offer public and private funding models for AI research that India should consider.

3. The sequential system of education and work is outdated in today's economic environment as the nature of jobs shifts rapidly and skills become valuable and obsolete in a matter of years.

Indian Banks and the Technology

The balanced approach followed by Indian central bank, Reserve Bank of India, is another major factor in any new technology adoption in Indian banking sector. In the last few years-RBI has taken a cautious but pragmatic view of embracing new technologies, often forcing technology adoption on banks through regulation, wherever it has seen scope to enhance customer experience and efficiency using a particular technology. RBI's proactive push of new technology adoption has not just been restricted to creating policy frameworks. It has also used a mix of regulatory framework, various initiatives and even worked with the industry to make things easier and effective.

The creation of National Payment Corporation of India (NPCI) which has significantly brought down the cost of electronic transactions is a paradigm shift in Techno Ambience. The regulator also has an academic/research unit, Institute of Development and Research in Banking Technology (IDRBT) which keeps studying the opportunities and challenges in new technology areas. It is not a coincidence that both these units have been actively involved in testing out blockchain as a proof of concept.

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