US-based information technology company Synechron Inc. serves the banking, financial services and insurance (BFSI) sectors. Founded in 2001, the company has more than 8,000 employees across 18 countries. In India, it has presence in four cities: Pune, Bengaluru, Hyderabad and Chennai.
The company is using gamification to incentivise sales performance in the insurance industry, and is deploying virtual reality to tour customers through property as well as offer them a VR-guided retirement journey.
In an interview with TechCircle, co-founder and chief executive Faisal Husain says the firm is keen on blockchain, and has developed cloud solutions that show how the distributed ledger technology can overcome real-world challenges faced by the BFSI industry.
Husain, who has worked with data and analytics company Dun & Bradstreet Corp and wealth management firm Merrill Lynch, also believes that blockchain alone can add up to $5 billion to India’s economy in the next five years. Edited excerpts:
In what ways is blockchain disrupting the BFSI industry?
Blockchain is evolving rapidly with numerous financial institutions testing various use cases that can transform the way business is conducted and processes are implemented. Financial services organisations are looking at how to apply blockchain’s distributed ledger and smart contract features to build applications in trade finance, where double fraud is a major concern, mortgage, peer-to-peer lending, remittance, global payments industry, and others.
Our experts have developed blockchain accelerators -- working solutions on the cloud -- that show how blockchain can overcome some of these real-world business challenges.
However, blockchain is still in its nascent stage and is yet to see mass adoption across the industry. While working with our global clients, we are seeing firms advance from the proof-of-concept stage to pilots and field trials to enterprise-production plans.
Lately, many Indian banks have started experimenting with blockchain for cross-border transactions and trade finance.
There is a lot of talk that blockchain holds huge potential in the subcontinent. Are public blockchains capable of bringing jobs and capital to India? How is India positioned in the global blockchain market?
With the government of India’s push to explore the implementation of blockchain across industries, there is heightened demand for blockchain architects. The pilot project commissioned in 2017 by the government's policy think-tank, NITI Aayog, called IndiaChain, will also help create jobs in the sector.
Fintech firms are looking for blockchain developers, blockchain system architects, and blockchain software engineers. A recent report suggested India could be among the world’s blockchain leaders by 2023 with the right investment, an inclusive ecosystem and favourable regulations. While financial services will continue to lead blockchain adoption, other sectors, namely, healthcare, manufacturing and energy will follow suit.
India is the world’s largest recipient of remittances, which make up a huge chunk of the balance of payments. Blockchain can help reduce overseas and domestic transaction costs, know-your-customer (KYC) charges for banks and help prevent financial frauds. Due to these features, the National Association of Software and Services Companies (Nasscom) believes blockchain can add up to $5 billion to India’s economy in the next five years.
What is your take on the potential of gamification in the BFSI sector in India? How are you incorporating this in insurance sales management?
Financial services and insurance firms are adopting gamification as part of their digital strategy. The modern banking customer is open to newer technologies that make life easier.
Next-gen fintech firms are adopting gamification-as-a-service for customer engagement and marketing strategies. Businesses can use gamification to educate people about their financial products and services through guided training programmes.
In the insurance industry, Synechron has built an insure-tech accelerator application – My Insurance App – which has a gamified leader board for insurance advisers to track their policy wins verses their competitors.
The wealth management industry is leveraging gamification to create targeted and personalised client experiences. For example, at Synechron, through accelerators from our FinLabs, we are using VR to guide customers through real estate property tours leading into a mortgage lending scenario as well as to experience a VR-guided retirement journey.
With the BFSI sector adopting new technologies, there is also concern over how secure the platforms will be. What are the security prospects for emerging tech in the sector?
As legacy businesses move towards digital, cybersecurity will become a topic of boardroom discussions. The role of the chief security officer is fast-evolving owing to the increased number of sophisticated cyberattacks. When it comes to financial institutions, the attack on Bangladesh central bank in February 2016 made cybersecurity a top priority globally.
One of the latest trends, as businesses have moved towards a streamlined technology and development operations approach through DevOps, has been an area called DevSecOps. DevSecOps is all about placing continuous security awareness and testing in the continuous development and release cycle.
The ever-evolving cyber-threat landscape is making sensitive information vulnerable to data exfiltration (unauthorised copying, transfer or retrieval of data from a computer or server) and theft. A layered security policy needs to be put in place to exercise stringent control over the bank’s networks.
Last year, around 10,000 banking frauds occurred in India. While there has been increased awareness to regularly update the cybersecurity infrastructure, most banks are yet to devise a rapid response plan to deal with cyberattacks.
What are the common misconceptions associated with Natural Language Processing (NLP)? How does NLP differ from Natural Language Understanding (NLU)?
NLP is often confused with NLU. While both use algorithms to process large volumes of natural language data to reduce dependency on humans, NLU is a subset of NLP and focuses only on comprehending and categorising the text body.
NLP ingests the data structured by NLU from the body content and ultimately is used for human-machine interaction via chatbots, voice assistants, and in voice recognition software.
NLU builds upon NLP by adding a qualitative, linguistic approach to language processing. NLU-based agents understand user intent and, as a result, can have fluid conversations -- a capability that is essential in service domains.
How different is NLP from NLU in terms of processing, technological advancement and in ease of extracting insights? Does NLU require more processing power and more complex tech?
NLP considers all of the systems that are working to tackle end-to-end interactions with users, in their language of choice. NLP requires context to interpret human words logically. It is comprised of NLU and Natural Language Generation (NLG), which converts unstructured data into structured form – the machine language – and handles queries by the end-user.
Computational linguistics is used to paraphrase a human sentence into a semantic format and convert it into a programming language. NLU is used to provide a structure to the data and analyse the user’s tone and intention. For example, NLU would help differentiate the noun pin from the verb pin.
Most of the data that exists today is in the form of texts. Therefore, the goal of NLU is threefold. The first task is domain classification. For example, is a user talking about airlines, programming an alarm clock, or dealing with a calendar? It can identify single as well as multi-domain data.
The second is user intent determination. For example, what general task or goal is the user trying to accomplish? For example, the task could be to find a movie, or a convenient flight, or delete a calendar appointment.
The third one is slot-filling. For example, how does the system need to act to accomplish the task? Extracting particular slots and fillers that the user intends the system to understand with respect to their intent.
In the technology sector, for example, a simple password reset request may require multiple clarifying questions about app version, role, or operating system. NLP alone can diagnose that the intent is resetting a password. NLP and NLU can both diagnose and resolve the issue.