Bahwan CyberTek (BCT) is a global technology company specializing in innovation in IoT and Predictive Analytics. BCT provides state-of-the-art solutions for banking and financial services institutions especially in the domain of ‘Regulations Technology’ based on platforms created through emerging technologies.
Jaya Vaidhyanathan, President, Bahwan CyberTek, is one of the few women leaders in the banking sector in India who has over two decades of experience in spearheading functions like Financial Services, M&A, Risk Management, Outsourcing Advisory & Technology across leading companies like Accenture and Standard Chartered Bank. Apart from managing Bahwan CyberTek, she is also on the board of Mahindra Sanyo Steel, Altran and Spice Mobility.
In an interaction with VoicenData, Jaya Vaidhyanathan goes into every detail of technologies and solutions that are disrupting the banking and fin-tech sector. Few excerpts:
VoicenData: The fin-tech industry is notably the biggest adopter of technologies like artificial intelligence, machine learning, predictive analytics and blockchain. How according to you are these technologies deployed in the industry?
Jaya Vaidhyanathan: Each of these technology implementations can have different outcomes. As a developer of digital solutions for the banking sector, Bahwan CyberTek has made thorough studies on how AI, ML, blockchain, etc. play a significant role in day-to-day secure banking operations. I am going to break down each of the technologies and explain in detail on its role. A common pattern seen in the use of these technologies is the collaboration between banks and FinTech players, with some banks opting to buy or own FinTech arms, while others opting to work with them separately. This is proof of the fact that technology has moved out from being a support function to a business enabler. I see 3 critical technologies enabling this:
The first one being Artificial Intelligence: There are 3 top reasons for the rise in its adoption:
- Prevention of frauds and anti-money laundering: Most banks globally are moving from a rule-based system to an AI-based system for the identification of money laundering events. Information from geo-tagged data, typical customer transaction patterns, and their current transactions are compared and flagged. For example, if there is an unprecedented withdrawal of cash, then, this could be one indication of pattern mismatch.
- Algorithmic trading: Real-time data from various financial markets are analyzed to arrive at the mood or sentiment of the global market. Therefore, a more accurate prediction of the relative trading prices in these markets is possible with AI and this in-turn helps in making informed decisions without individual bias.
- Risk Management: AI greatly helps in providing an unbiased risk profile of clients and this information can come from both internal and external sources. Data related to the latest transactions, market trends, commodity prices, and other sources are gathered to arrive at an unbiased risk profile that helps in decision making on loan processes.
The second technology is Machine Learning for unmanageable data: Machine learning applications are those involving computational logic to be run on large data sets unmanageable by humans. What is “unmanageable” here is both the volume of data and the continuous need to refine algorithms that are needed to process such data for meaningful analysis and actions. Examples include how machine learning is used for customer service and say credit appraisal. Chatbot deployment has also made significant in-roads into banking services. Though Chabot still has a lot of scope to mature, yet, it perfectly fits the need to fetch information, help customers with transactions or recommend products. It substitutes the need for human intervention in these cases. In Credit Appraisal, ML powered engines can today digest customer data and publish results on credit applications instantaneously compared to the waiting time they had to endure in the past. This is good for both customers as well as banks.
Predictive analytics for proactive planning is another need of the hour. Today, models can help bankers take informed decisions without bias with only hard data as a reference. This is possible with greater availability of customers’ and macroeconomics’ data, as well as computational power that helps analyze hundreds of variables. This allows us to choose from among them those factors that have a meaningful impact on a system. Applications of predictive analytics are vast, with some examples including default prediction (for ex.: how likely is it that educational loan customers from a particular region will default), business planning (how many branches should a bank open in the next 5 years), stress testing (how much capital should one raise over the next 3 years in the event of a recession), etc.
The third and much talked about technology in banking is Blockchain. Many a times blockchain is confused with cryptocurrency. Cryptocurrencies have been very volatile, where investors have lost a lot of their money over the last three years. Whether cryptocurrencies will survive or thrive is still being debated, FinTech applications of blockchain are far more powerful. Some of its applications would be:
- Cross border transactions: Many Indian banks have already built solutions that are proof of concepts of cross border payment transactions using Blockchain and these have already been successful. The strength of Blockchain can be used to greatly cut transaction costs in cross border payments where trust levels are low, leading to high counterparty default probability as well.
- Funding platforms: 11 Indian banks have come together to form a small & medium enterprises funding platform using blockchain. Under the forum named Blockchain Infrastructure Company (BIC), these banks will share customer data, which will help the banking system do more business with creditworthy customers, and also help customers get access to cheaper funding.
- Smart contracts: A contract has always needed a broker/intermediary to uphold its validity, and this broker eats up a large share from the contact itself. Smart contracts are blockchain enabled contracts where technology performs the role of verifying and enforcing contracts without the risk of them being tampered. This can be a game changer and can bring higher reliability in contracts and reduce transaction costs.
VoicenData: The next question is an inevitable one! Can you share your perceptions on how the launch of 5G will have an impact on banking operations?
Jaya Vaidhyanathan: Mobile devices are increasingly becoming the centrepiece of banking transactions. While the first shift was from branch banking to desktop banking, we’re now seeing mobile-first strategies from banks, wherein customers expect to just download an app from the play store and start banking. This brings in technological elements unique to mobility, into the banking space. While banking on mobile devices still has its own problems, the rollout of 5G is expected to be a game changer.
The potential impact of 5G on banking can be gauged by the fact that while widespread prevalence of 5G is expected to take many years from today, RBI’s Institute for Development & Research in Banking Technology (IDRBT) has already set up a lab providing an ecosystem including regulators, technology organizations and financial institutions for use cases of 5G technology in banking.
Areas where 5G will make a difference: Since 5G is expected to be faster than 4G, it is expected to usher two changes:
- Connect more devices at affordable prices and with lower power consumption
- Dramatically reduce latency – the time taken for a device to communicate with a distant server and get back a response.
These properties lend 5G useful in a variety of applications as follows:
- Financial Inclusion: Connectivity can increase banking penetration, through mobile apps or doorstep (tablet), into the population of the country which still does not use a bank. Rural India skipped the traditional migration path from landline telephony to pagers to mobile telephony, by directly going mobile. Similarly, rural India could also leapfrog in the banking space directly to mobile device-based banking without going through branch banking.
- Remote Banking: Doorstep banking, but at the higher end of the customer spectrum where the focus is more on customer convenience rather than access to the banking system. This would mean, for example, no more rushing to the banks during lunch time from office for banking transactions, and instead, a banker would simply turn up at the customers’ workplace with a tablet to perform even the most complex of transactions requiring physical presence.
- Data mining and AI: With greater availability of data on customers, technologies like AI can be utilized effectively by financial institutions for applications such as risk management and customer reach. This will be made possible by greater computing power being networked with low latency.
- Point of Sale: PoS (Point of Sale) machines currently working on 2G which is about to be phased out, will make way for 5G machines with much lesser transaction failure rates.
- Open Banking: the future where banking applications can talk to each other in an ecosystem consisting of banks and Fintech players using APIs – will be achieved through zero waiting time possible with 5G. Third party applications accessing banks’ databases would mean truly anywhere, anytime payments, hastening the death of cash.
- IoT and Cloud: Ease of networking daily use devices around us will mean access to data on what customers do every day, which can also be stored centrally for data mining.
- Multi-layer authentication: With increased digital penetration comes the increased risk of online fraud, and having multiple levels of authentication, possible with better connectivity, is the solution. A customer could be given secure access to a banking platform on a mobile device through a combination of biometrics from different devices, behaviour analytics and so on, authenticated with credentials stored on the cloud, at real time.
- Personal Finance Management: Online, real-time feedback and recommendations to customers based on their financial transactional behaviour will be made possible through 5G. Such an application could warn users of overspending towards the end of the month, for example, based on what they’ve spent that month.
- Trading applications: The one place where latency has always been talked about is the stock market. 5G can truly revolutionize this space by bringing retail traders closer to institutional players, thereby ensuring efficient markets.