AI Along With Machine Learning For Rural Population Across India

Introduction

Indian banking landscape is seeing massive transition with the advent of financial inclusion through RBI. As the government shifts focus toward cashless society, it also pushes a bouquet of digital payment options in the form of schemes, apps and services like Small savings accounts, Agency banking channel, Aadhaar number, Pradhan Mantri Jan Dhan Yojana, BBPS, BHIM, AEPS payments and UPI. Though the journey has just begun it may be wise for the Banks and regulation to think of ways to take automation ranging from MSMs to rural hinterland. Technology a decade and half changed the landscape of banking in India and is again set to revolutionize the entire process of financial inclusion and this time AI is all set to augment the idea. Banking the UnbankedIn India, a large fraction of 1. 3 billion people are still not able to access basic banking services.

For instance in 2017 the Global Findex data showed how 5% of Indians accessed a financial institution account from their phone or the Internet, and only 2% of the population owned a mobile money account. Comparing this to sub-Saharan Africa, where 21% of adults had a mobile money account in 2017 and a 50% increase since 2014, one can see how the India is quite backward even compared to other developing regions across globe. Similarly for Digital payments it was observed how 97% of adults in Kenya making a digital payment in 2017 and 60% in South Africa, compared to 29% in India. Given the fact that rural India saw a growth of 15 per cent in mobile internet in 2017 with mobile internet users’ figures nearly touching 187 million in rural area alone, the prospective of mobile banking will be foolish to deny. Corroborate this with the argument that providing the right information to consumers (both financial and non-financial) is likely to increase consumer satisfaction.

An example of a bank that is offering value-added services is ICICI Bank in India, enables Facebook users to link their debit card to their profile in order to recharge their pre-paid mobile phone connections, borrow and lend money with friends and buy movie tickets. In Turkey, DenizBank is offering banking services through Facebook: customers can connect to their Facebook account and access their bank account to initiate wire transfers and manage daily expenses by monitoring their credit cardsEnter AI and Machine learningIn rural India where there is hardly any credit history for prospects for small loans, AI can create credit score / credit worthiness scores using data from Aadhaar, farming turnover, affordability (mobile being used, mobile bills, recharge frequency), social network (social media, cell phone call logs), travel information (GPS data, google timeline) and other such features using predictive modelling and Machine Learning algorithms. ML algorithm can eventually build credit profiles for those who were never exposed to banking system and remain excluded from ‘financial exclusion’.

Loan Frame uses ML to access credibility of their customers. In another case, companies like Monsoon Credit Tech use AI to determine credibility of MSMEs. Using AI, many countries have started giving cash-flow based loans to MSME by learning patterns from various unstructured data sources including transactions, purchases, financial statements, tax statements and various other documents. These data helps ML to predict financial situation of the company and prescribe repayment methods. For instance, Kopo Kopo (by Grow) in Kenya which is exploring capital requirement of MSME is automatically decided and kept apart for repayments without creating a dent in the cash flow. Many other Fintech around the world have confined every documentations had approval over a mobile app for loans by analysing streaming cash flows of MSME using data from various digital wallets. With advent of PM Jan Dhan accounts which are linked to Aadhar numbers, ML can be used to predict and prescribe the right products to the customer. The transaction behaviour of these Jan Dhan account clubbed with techniques described above ML can be used to find out right products for people in remote areas. Clubbing AI with Blockchain technology can break the barriers to accessing financial services for rural areas. Blockchain technology clubbed with AI can be used to create digital blueprints of customers who may not have relevant documents for availing banking facilities.

Oradian along with Blockchain platform Stellar brought low cost micro-payments in Nigeria. This was done by integrating the Stellar platform into their core software. Oradian will allow 300,000 Nigerians to cheaply transfer money between MFIs over the Stellar network. This network reaches a total of 200 branches and serves over 300,000 end clients (over 90% are women), mainly in rural areas across Nigeria. The Fintech and bank hand in hand would save millions by using AI w. r. t cost of acquiring customers and lowering default rate, a win-win situation. The bank can further use reinforcement learning technique to make AI smarter with every new transaction. Given the fact that AI and ML can use data from sources that were in past never imagined, sensitive banking decisions can be easily made. One example being data from GSTN being used by Ai and ML to create convincing case for financial support for MSMEs. Not only a credible case being presented, but the time taken to create such cases cab be reduced from months to few days, thus helping MSMEs t have quick access to financial support and reducing red-tapism. AI breaks language barrierAI can be of assistance to Fintech and banks in creating better customer experience without recruiting myriad of agents. AI can easily use NRLP (Natural Regional Language processing) to access rural areas (where the main language of communication are regional language) from toll-free mobile number at any given hour of the day. This would not only enhance customer experience but can be used to even communicate in local dialects. It will save travel cost and time for people residing in rural areas. Moreover, using voice fingerprint techniques for speaker recognition the prospective customer can even subscribe for various banking facilities.

In one example, the patented Phoneprinting technology by Pindrop’s innovative software can identify 147 different features of a human voice from one call in order to create an audio fingerprint of that caller and looks for unusual activity, potential fraud so as to trace unsolicited callers. It integrates with companies internal systems and identifies people's voices, locations, and devices. This is added to a database for future reference and to help separate legitimate callers from scammers. Santander and HSBC both launched voice banking technology on their mobile apps in collaboration with Nuance Communications which are intended as an additional layer of biometric security for customers and as a management tool for their finances. By analysing over 100 factors, including speed, cadence and pronunciation, individuals can make payments, report lost cards, set up account alerts and answer questions about spending.

Recently, Bank of America launched a financial digital assistant called Erica while other banks like UBS, Credit Suisse and JPMorgan are using virtual-advisors that makes the use of cognitive and Machine learning to guide customers with financial planning and investments. One such breakthrough is Amelia that was able to manage 65% of the most common user queries in under four minutes instead of the average 18 minutes it took the existing staff. Importantly, for the banking industry, Amelia can execute all of the key customer related processes without ever deviating from the rules. 'Luvo' is an online virtual assistant designed using technology from IBM's Watson AI system. It was recently rolled out by the Royal Bank of Scotland (RBS) and NatWest to interact with customers and handle simple problems via a web chat tool. AI can see this too!Going deep into rural requirements, AI clubbed with computer vision and deep learning can utilize farmers’ geo-spatial data, pictures of their farms/fields, historical yield, land records, weather and soil data, technology used, remote-sensing data to come up with right financial advice for farmers by keeping in account risks and sell potential. These data can also be used by Fintech to optimize insurance and other financial benefits. Moreover, farmers who are Illiterate and may not understand the fine prints of documents can be assisted with reading a contract using the text to speech feature. In another example, MSMEs are being reached for financial services by accessing the movement of products from shelves (using deep learning and computer vision) to estimate financial stability and repayment potential of customers. Services like FIBR assists Fintech to use AI (computer vision, predictive analytics and natural language processing) financial services for MSME sectors.

Other example would be Tala, for example, uses non-traditional data to predict credit score of those who are not covered by credit bureau by looking at data like ‘social connections, texts and calls, merchant transactions, app usage, and personal identifiers’. FinChatBot, Teller which allows fintech to communicate automatically through message and present personalized advice and Kudi. ai which uses chat messengers to send money to any bank & pay bills, are tackling this challenge head-on given the potential benefits. AI systems can further analyse tax data and build quire accurate probabilistic model by merging these data with postal codes, cell phone records, travel patter, social network etc. With the rollout of GST which had made it mandatory for tax filing to be online, these analysis can be generalised across industries, geographies, demographics or combinations. Banking Advancements across Globe Taking an example of TransferWise (an international money transfer portal) lowered the cost of remittance down to around 1% thus allowing millions of people to transfer money across nations without heavy charges. Extending the thought, a huge remittance potential lies in rural areas as more than 100 million people migrate from one village to other for work can be brought under banking system for proper remittance. A study by National Remote Payments Survey by National Council of Applied Economic Research (in partnership with Nielsen’s forecasting technique) states that domestic remittance is around 1000 billion per year where rural India contribution is around 60-70% but only 30-40% of these remittance ate done using proper channel. Open banking aka open API that is being implemented across UK and EU would allow a bank “to access native fintech solutions in a plug and play type way”. In a simple case Experian Connect API gives power to customers to view their credit score instantly without any hassle. Banks and customers can not only use it for tracking their credit score but can also plan its course for improvement.

The website states that “Consumer-empowered sharing allows you to create products and services for previously unreachable markets”. It further allows “landlords, property owners, real estate agents and other small business professionals the ability to view a credit report online” thus enhancing the reach and scope of credit scoring. Similar transactional behaviour can allow an ML algorithm to learn more about spending pattern of an individual and thus predict better recommendations for products and credit score, to say the least. This also allows easy portability of bank accounts and banking services (the way mobile number portability works) as Open banking would allow various system and databases to talk to each other seamlessly. Paytm in India (during demonetization) was a life saver for MSME. AI and ML if clubbed with open API would allow fintech to offer various other banking services using data from likes of Paytm. ML can peruse customer data like spending habits to advise financial planning. ML can enhance identity profiling in multiple ways. AI will not only decrease transaction-fraud detection by taking various features other than customer details (like geo-spatial data, key strokes, timings, monetary movement, device details etc) to verify the identity of a person on real time thus validating real time transfers.

AI can use network analytics to understand and predict the upcoming fraudulent transaction by learning the pattern from fraudsters. AI can be used to read financial pattern by looking at mobile bills, SMSs, call records, mobile browsing history, frequency and distance travelled thus creating deeper user profiles. In an example from Africa, ML allows small rural workers to get their transaction approved without delays and get their money transferred to their account from as far as America almost instantly. Data collected through the IoT can aid fintech in making better decision by using ML to dive deep into customers’ spending patterns, transaction pattern thus boosting rural banking. The best attribute of Open Banking is eliminating multiple mediators and connect banking services directly to customers thus reducing costs of money transfer.

Conclusion

As banks focus towards efficiency, customer delight, experience, their use of innovative technology is crucial. Artificial intelligence (AI), or the use of computers to carry out the processing and decision making tasks previously carried out by humans, is one form of innovative technology banks are adopting at a very high rate to achieve these objectives. The steady increase of AI in banking, however, will likely have both positive and negative impacts on the banking industry. Learning from First National Bank of Wynne who began using AI for conversion assistance, it has decreased conversion costs by 70%. Visibly, as the efficiency rises AI will have a tangible and optimistic influence on banking.