CXO of the week: Varun Bhalla, Country Manager, India, Provenir

Varun Bhalla has shared his views on key offerings and key USPs of Provenir, his entrepreneurial journey, the company’s growth, and much more.

Manisha Sharma
New Update
Varun Bhalla

Provenir helps fintechs and financial services providers make smarter decisions faster with our AI-Powered Risk Decisioning Platform. Provenir brings together the three essential components needed – data, AI and decisioning – into one unified risk decisioning solution to help organizations provide world-class consumer experiences. This unique offering gives organizations the ability to power decisioning innovation across the full customer lifecycle, driving improvements in the customer experience, access to financial services, business agility, and more.


VarunBhalla serves as Country Manager for Provenir India, overseeing all business operations including sales, pre-sales, delivery and customer success. Varun brings more than 16 years of decision analytics technology experience in sales, business development, consulting, product management, and channel development.

Recently we have engaged in an interview with Varun Bhalla, Country Manager, India, Provenir. He shared his views on key offerings and key USPs of Provenir, his entrepreneurial journey, the company’s growth, and much more.



VarunBhalla serves as Country Manager for Provenir India, overseeing all business operations including sales, pre-sales, delivery and customer success. Provenir is a global leader in data and AI-powered risk decisioning software which helps fintechs and financial services providers make smarter decisions faster.

Established in 2004, Provenir works with disruptive financial services organizations in more than 50 countries and processes more than 3 billion transactions annually.

Give us an overview of Provenir’s products and services, and presence in India.


 Proveniris an industry-leading risk-decisioning company that provides a cohesive risk ecosystem for businesses to make faster and smarter decisions across identity, fraud and credit; providing deeper insights, and automated and optimised decisions, with a feedback loop for continuous improvement. This enables our customers to better assess risk and monitor for potential fraud. Alongside our data and AI-powered platform, we also provide the Provenir Data Marketplace, a one-stop hub, accessible through a single API, that gives our customers quick access to various data sources including, open banking, KYC, fraud data, credit risk, verification, social media, analytics, auto, affordability and more.

We have more than 100 data vendors in our Marketplace, with about 25 focused on Indian data across all the above categories.

We have also partnered with AMU Leasing, a women-led non-bank lending startup in India. Through this partnership, AMU Leasing has developed a fully automated underwriting ecosystem for frictionless leasing, financing and purchase of electric vehicles, which has helped many unbanked communities in India to afford their first vehicle.


How has Covid-19 accelerated the transition to digital for the traditional financial industry? What will be the new normal in the industry?

The Covid-19 pandemic has brought disruption to the world, and for financial services organisations, this meant having to adapt and innovate quickly and digitally transform their business for an online audience.

This digital shift shone a spotlight on credit risk models, and according to a survey by  Pulse, only 16 percent of fintech and financial services organisations in Asia Pacific believe their credit risk models are accurate at least 76 percent of the time.


With consumer behavior changing at an unprecedented pace, business leaders, especially in India,realised the shortcomings of their legacy approaches. Businesses needed ways to automate workflows to circumvent business constraints as well as stay ahead of market trends. To make all these possible, businesses needed to level up their decisioning, harness forward-looking

predictions and utilise real-time decisioning. Financial services organisations in India also recognised the potential of artificial intelligence, machine learning and alternative data, and were looking to deploy them for credit decisioning.

By embracing alternative data and advanced AI and ML technologies, organisations will discover newfound agility and confidence in their credit risk models and are better prepared to respond to changing market forces. At the same time, the business is also able to support the necessary industry practices such as fraud prevention and financial inclusion. Better credit risk models will also enable the enterprise to discover new and untapped market segments, and bring new products and services faster to market.


How to power financial inclusion with alternative data and advanced analytics?

In the past, financial services utilised a traditional credit score to evaluate creditworthiness. Today, this has changed. Alternative data creates a more inclusive approach to credit risk decisions by allowing organisations to consider new data points throughout the process. The right alternative data combined with advanced analytics or AI allows for a more comprehensive and accurate credit risk assessment to better serve those whom traditional data deems unqualified.

In India, the government and the central bank have taken forward-looking measures including launching IndiaStack, to enable the dynamic growth of United Payment System (UPI) transactions and drive the adoption of Account Aggregators. This decision will enable the partnership to unlock a large quantity of alternate data, usable and meaningful to the credit risk decisioning process.


What, as per you, are the five important things that fintech should be looking at today?

Data is power. Fintechs need to understand the power of data and focus on extracting the value of their data. Firstly, businesses need to consider how they can build a 'single customer view' (SCV) into their current ecosystem. This enables the business to map out a customer's journey, and deliver value to the customer through out that journey. A data aggregation layer in the backend, paired with decisioning capabilities can enable the business to interact with the customer in real-time, and deliver a better customer experience.

Secondly, fintechs needs to consider how to harness artificial intelligence in credit risk decisioning. An AI-powered data and decisioning platform offers the enterprise the flexibility to iterate, expand and scale according to their own timeline. When data, AI and decisioning are unified in a single platform, businesses can form a single source of truth that enables them to innovate in ways that traditional data analysis and decisioning cannot.

Thirdly, no-code platforms have enabled businesses to reduce dependence on vendors, address talent challenges and alleviate development workloads. When paired with a risk decision platform, financial institutions can develop powerful solutions with user-friendly visuals. Such platforms also enable developers to quickly make critical changes as well as fuel faster integration and automate workflows and analytical processes. And that's just the tip of the iceberg.

Fourthly, data-as-a-service provides great utility for credit risk decisioning, especially if that data is sourced from around the globe. When businesses can access a one-stop hub for data through a single API, this expands organisations' capabilities and offerings. Through a wider selection of data, businesses can expand across borders or regionally, and offer their existing products and services to new customers, or simply unlock a new market segment previously beyond their reach.

Finally, fintechs need to consider what makes their business agile and resilient in the face of changing consumer behavior. Having access to real-time data will enable the business to make the necessary pivots on a day-to-day basis, enabling better and more consistent business outcomes. At the same time, businesses can use real-time data in customer interactions, and thus provide better customer service, generate desirable product offerings instantly, and provide better conversion to sales. Ultimately, real-time data enables the business to be agile and accurate in their credit risk decisioning, and improve customer engagement.

How can small financial players overcome the challenges faced while adopting technology into their businesses?

Smaller organizations need to be maniacally focused on efficiency, agility and innovation. They need technology that can easily scale as they grow and provide the flexibility to support new products and adjust to changing market conditions. A low/no-code interface will allow them to integrate systems, change processes and launch new products quickly and without vendor reliance.  A single,comprehensive cloud-based solution—not a selection of vendor products tied together—that unifies all risk decisioning, data, and AI through a centralized user interface (UI) will shorten development lifecycles and help them get products to market faster. Working with a partner who can help them anticipate and prepare for future needs and support them throughout their journey is also invaluable.

How can AI-powered risk decisioning play a part in transforming the entire credit risk decisioning process?

Artificial intelligence provides the business with more options beyond traditional decisioning capabilities For .example, AI has high utility when dealing with unbanked customers; enabling the business to use alternative data and deliver superior customer experiences across the customer lifecycle. Instead of relying on traditional credit scoring methods, AI solutions will identify new patterns for a more optimised decisioning method. This also enables the financial institution to expand its customer base while effectively managing risk. At the same time, self-learning AI models can spot irregularities instantly - minimising the occurrence of fraud. AI analytics also empowers an organisation with data insights, enabling personalised pricing for each unique application while increasing profitability for the enterprise.

Ways that AI can impact the entire customer lifecycle?

AI has the ability to power performance improvements across the customer lifecycle in multiple ways.

With AI you are no longer confined to pursuing customers with the attributes of your existing lending base. Instead, you can use AI models to discover new patterns in the data that empower you to lend to a much wider base of people. It’s a quick way to drive business growth without increasing costs or risks.

AI also supports financial inclusion. Approximately 3.6 billion people in Asia have no access to formal credit. Financial services organizations typically struggle to support these consumers because they don’t come with a history of data that is understandable by traditional decisioning methods. However, because AI can identify patterns in a wide variety of alternative, traditional, linear, and non-linear data, it can power highly accurate decisioning, even for no-file or thin-file consumers, giving organization the opportunity to support unbanked and underbanked consumers on their financial journeys.

Expanding your existing customer relationships with personalized upsell and cross-sell offers is another benefit of AI. With the right AI models and automated decisioning, organizations can analyze customer data and automatically make the correct upsell and cross-sell offers when they are most likely to convert.  These are just a few examples of how AI can enhance the entire lifecycle of customers.