Introducing Inquisitorial by Indianaut, a long-form newsletter where we explain and analyze important stories stemming out of the Indian entrepreneurial ecosystem & economy. New articles every Saturday & Sunday.
In our previous article, we looked at how the new age of AI has dawned upon the Indian fin-tech ecosystem and how various entities such as the government, judiciary and the private sector are adapting to the new advances simultaneously as per their functions.
It can clearly be seen that fin-tech and AI when assimilated together have genuine, real world application. The global AI in the fin-tech market was estimated at USD 6.67 billion in 2019 and is expected to reach USD 22.60 billion by 2025. A recent study titled ‘State Of Enterprise AI In India 2019’, published by Analytics India Magazine in association with BRIDGEi2i, suggests that the Indian enterprise market for AI applications is estimated to be valued at $100 Mn, growing at 200-250% CAGR.
Another report by Accenture shows AI has the potential to add $957 Bn, or 15% of India’s current GDP to the economy by 2035. The combination of the technology, data and talent that make intelligent systems possible has reached critical mass, driving extraordinary growth in AI investment.
In India, apart from typical fin-tech startups started by first-time entrepreneurs, there are a growing number of fin-techs by seasoned entrepreneurs such as Sachin Bansal (ex-Flipkart founder) and his firm Navi Technologies.
Increasing demand for automation among financial organizations is one of the major market drivers of artificial intelligence in financial organizations. Efficient processing of data, owing to complex AI algorithms, has allowed management teams to make more informed, data-driven decisions in the fin-tech industry in India. This can further be broken down and looked at from a sub-sector aspect:
1. Financial Markets & Wealth Management
India has witnessed many advancements in the wealth management industry. We have been witnessing the democratization of investment advisory services, where wealth managers are leveraging technology to offer low-cost investment advisory to mass segments.
Robo-advisory in India is rapidly evolving. We are seeing startups going beyond Mutual Fund distribution to offering digitized, long-term financial planning. They are using algorithms and artificial intelligence to understand the goals and aspirations of users better and provide them with personalized advice rather than just offering a generic portfolio. As more and more millennials pick up stock market investing and other investment avenues, financial literacy is also improving, leading to a mature outlook towards aspects such as financial life goals and retirement planning. The services of these platforms range from automated plans, goal-based asset allocation, and end-to-end advisory based on information taken from the client. Today, not only startups but also established financial advisory services, such as Birla, Bajaj Capital, ICICI Securities, and Sanctum Wealth Management, are optimistic about the future of robo-advisory. The competition in robo-advisory is resulting in the expansion of the wealth-tech market.
A gamut of hedge funds are utilizing the merits of AI in analyzing large amounts of data, predict imbalance in supply and demand framework, and forecast market trends. AI is also deployed for textual analysis of news stories and financial releases, to generate positive or negative trading signals. A survey by BarclayHedge last year indicated more than half of hedge funds around the world are currently using AI or machine learning to help make investment decisions, and a quarter of the money managers are using it for trade execution. Renaissance Technologies, Bridgewater Associates and DE Shaw are some hedge fund stalwarts in the industry assimilation of AI technology.
2. Chatbots & Virtual Assistance
AI is increasingly being used in customer relationship management. According to Gartner, by 2020 consumers will manage 85 percent of their relationships with the enterprise without interacting with a human. Fin-tech firms are also making use of customer-facing systems such as chatbots or voice systems capable of providing human-like interaction with consumers to effectively resolve issues at a fraction of the cost, no matter the time of day.
Chatbots bring better customer experience allowing insurance firms to deploy distribution, claims, and customer service. Chatbots help in functions like general customer service questions, personalized product recommendation, general questions from agents/brokers, direct-to-consumer (D2C) sales, claims, and more. AI can also help with customer retention and loyalty, as it can take a customer’s information into account to make sure that businesses are offering the most suitable products at the right time. This gives the companies the opportunity to improve their services and offerings, thereby aiding customer loyalty.
NLP(Natural Language Processing) based chatbots offer personalisation between the customer and the service team dynamic. The goal is to provide a one-on-one experience thus building a psychological rapport and boosting credibility with the customer to customize their preferences. A famous example of this would be a mobile app by Sun Life, named Ella. Ella is a virtual assistant built on NLP frameworks who helps users who retire by staying on top of the insurance plans.In addition, Ella can send you personalized tips and reminders about your coverage and plans – like health spending account balances, retirement savings options and more.
3. Insur-tech
Armed with the capabilities of AI/ML, predictive analytics, and data captured by IoT-driven connected devices, insur-tech players are exploring ways to make the most out of deep data insights and drive the transition from a reactive approach to proactive prevention. Insurance companies can leverage a data-driven, risk-scoring model, thereby enabling them to make better risk coverage decisions across all lines of businesses such as life & health, retirement planning, commercial, and investment.
Bharti Axa in collaboration with Policybazaar has launched a use-based car insurance that charges a premium based on usage of the car. It uses a telematics device to monitor how often the car is used and the number of kilometers driven. Edelweiss has partnered with Policybazaar to launch a car insurance policy that calculates premiums based on the age and experience of the driver.
4. Digital Lending
Digital lending fin-techs are targeting the unmet demand from Indian MSMEs as well as consumers for credit. Many banks in India have so far focused on highly creditworthy segments primarily due to a lack of credit history of others. The traditional ways of banking approve only upto 25 to 40% of the loan applications. However, with access to more data for credit scoring such as transaction, behavior, app-based data, location information, social data, and more, these new lending models aim to increase this threshold by an additional 10–15%, which is a huge market opportunity. From a small segment a few years ago, India now has over 338 lending startups.
The acquisition of Mumbai-based consumer lending platform PaySense by digital payments provider PayU at a valuation of $185 million, further brought the spotlight to the potential of digital lending in India. Several new models of digital lending have emerged, such as DMI Finance offering fin-tech startups API access to sandboxes, thus helping them develop bespoke financial products and Apollo Finvest positioning themselves as ‘AWS for Lending’ by enabling partners to offer digital loan products to their end-customers through APIs.
In consumer credit, the urban population is likely to leverage fin-tech lending services to avoid heavy documentation. This can further be witnessed in the workings of consumer-centric fin-techs such as ZestMoney, NAVI, etc. which process loans in under 10 minutes for 80% of the applications.
The rural population (which is new to credit) can benefit from alternative credit scoring mechanisms to avoid loan sharks. This would provide access to a market with over 300 million unbanked households. Hence, the use of identity, authentication, credit score, job eligibility, and social data to generate ratings for various use cases is likely to draw more attention in the near term.
5. Fraud Detection & Anomaly Analysis
AI and Machine Learning (ML) is being utilized by fin-tech organisations to gauge risk and assess fraud cases. Here, analytical tools are used to collect evidence and data is analysed, wherein AI tools learn and map out user behavior and seek patterns that can be used to identify potential fraud attempts. Over time, AI systems can learn and adapt to wean out undiscovered cases and refine fraud and risk detection capabilities to better protect consumers. AI frameworks are trained to detect any anomaly in on-going patterns of raw data at disposal. Some important use cases of sophisticated AI deployment would be irregular and fraudulent spending in credit cards, monitoring financial markets malpractices (insider trading), spotting irregularities in market movements that affect stock portfolios and money laundering detection. Its processing speed and self-learning capabilities are what makes an AI system so agile; this is primarily achieved via pattern recognition basis the data fed into the AI system.
When it comes to digital payments: the risk of fraud is shared on two shoulders - Merchants and buyers. In case of fraudulent merchants,a thorough scrutiny on their KYC and their online and offline footprint must be done. While fraudulent buyers use stolen credit cards wherein the merchant has to return money to the original card holder despite delivering the products.In these two scenarios real time monitoring using Machine Learning can help de-escalate the crisis situation.
(Source: Dogtown Media)
There is a rising consciousness globally around how AI can provide optimum results while working in tandem with humans. This essentially means that humans and AI augment each other’s unique capabilities; such as the innately human qualities of leadership, emotion, compassion, teamwork, creativity, and the speed, scalability, and quantitative capabilities of AI.
Going ahead, concepts like open banking, neo-banking, and API-based banking are getting ready to take the Indian banking industry to the next level of innovation. And like many experts and founders said earlier, AI will certainly be going to play a critical role in this transformation.
AI has already changed the way we bank and carry out financial transactions. Given the nationwide impact that adoption AI has had in payments, transfers, withdrawals, savings, insurances, lending and risk management, it has gone past the stage of just being a buzzword.
Falguni Chaudhary is a PR specialist and communications strategist. She has previously worked as a consultant for startups in the AI, sustainable fashion, ed-tech space and was previously a contributing editor at NewsD, Youth Incorporated Magazine and Feminism in India.
Quite an engaging series of articles.