Exclusive Interview with Rohit Rathi, Co-founder and CEO, KarmaLife

Globally, the market of finance apps is booming. The finance app industry is in the midst of staggering growth in global adoption, from 16% in 2015 to 64% in 2019, which is putting pressure on marketers to uplevel and digitize user acquisition and experience. KarmaLife aims to provide sustainable finance, a combination of liquidity, savings, and insurance to every gig & contract worker to stabilize & grow cash flows, strengthen their resilience, and unlock their aspirations. Analytics Insight has engaged in an exclusive interview with Rohit Rathi, Co-founder, and CEO, KarmaLife.

1. With what mission and objectives, the company was set up? In short, tell us about your journey since the inception of the company?

Our mission at KarmaLife is to provide sustainable finance – a combination of liquidity, savings, and insurance – to every gig & contract worker to stabilize & grow cash flows, strengthen their resilience, and unlock their aspirations. The key impediment to this segment's ability to access formal finance is not having adequate data points to assess creditworthiness, which is why we're building an alternative dynamic underwriting model linked to their work and earnings histories as well as other behavioral variables. Further, given the large trust deficit vis-a-vis formal financial products, we are reinventing the entire user experience journey to create more relevance, engagement, and reinforce trust.

We started our journey back in 2020 and have since validated a small-ticket, short-tenor credit product that allows users to access funds needed in between paychecks. Our research shows that up to 90% of workers run out of cash before their next payout cycle (see here) and this is an unmet need. Our earned wage access and line-of-credit solutions address this by balancing flexibility and affordability to users. Over the last two years, we have deployed these solutions across over 10 major digital gig platforms across use cases like e-commerce, food deliveries, ride-sharing, Flexi-staffing, merchant sales, and general logistics. We have seen significant user adoption, consistently high levels of credit utilization and repeat usage, and also deeper business benefits in the form of increased worker productivity and reduced churn.

Recently, we have augmented our product suite to include longer-term loans that enable gig workers to finance lump-sum monthly or quarterly expenses like a Vehicle EMI or smartphone purchase as well as a digital savings solution that offers 8-9% interest with full liquidity. Post completion of our latest round of investment, we have grown to a 40+ employee company and are investing heavily in the growth pipeline.

2. Tell us how your company is contributing using AI and how the company is benefitting the clients.

We are building a new paradigm in small-ticket finance for non-salaried blue-collar workers. Most of our users are young, hardworking, and responsible individuals who are steady earners and spenders but yet, remain credit Invisible (i.e., have no records of a traditional credit score). So, during times of crisis, while running short on cash, they are forced to rely on expensive non-traditional lenders or ask for financial help from friends and family, which results in escalated financial or emotional stress.

In such difficult times, Karmalife enables these workers access to flexible credit-on-tap credit, which in turn helps manage cash flows. The overall experience is also tailored to their needs with key features like digital onboarding, payout-linked repayments, UPI scan & pay on credit, and flat usage-based fees (without any interest or hidden charges).

We have a very strong data-driven culture with most of our business decisions being anchored in data. In particular, we have developed the following data-based features:

i) KarmaScore: This is our proprietary risk score driven by user permission alternative data. It's in line with conventional credit scores but what stands it apart is its exclusivity for KarmaLife users as it is graded on our financial products. This score helps us during the initial user onboarding process as well as estimates user-level risk continuously for future credit cycles by considering credit usage, repayments, and other financial behaviors. We use several machine learning algorithms to help us achieve this with transparent and consistent scoring rules. This enables 3-minute digital onboarding and thereby instant access to credit in times of user need. It also enables the financial inclusion of workers who would otherwise be left outside the traditional credit ecosystem. And finally, it helps build a credit history for new-to-credit or credit-invisible populations with a financial product that is closely aligned to their needs, aspirations, and cash flows.

ii) Dynamic credit allotment: We dynamically assign credit limits based on user performance at the workplace or past repayment. So, a user's credit limits can change, i.e., increase or decrease based on the latest information that is captured. To this, we apply several predictive machine learning algorithms. This allows us to build in natural incentive structures that reward users who have shown good "financial Karma" with higher or more flexible credits in future cycles, and at the same time manage downside risk. Overall, it instills and reinforces positive user behavior on the platform, creating a financially responsible user base.

iii) Fund projections: For better operations with our partner-NBFCs, we use various time-series forecasting models to predict future funds requirements. This allows us to optimize the float we need to carry to offer instant credit and ensure cost-effective operations. This in turn helps partner NBFCs to forecast their disbursements, and efficiently manage their cash flows. And on the other hand, it ensures Karmalife users have a seamless experience and can access affordable credit.

3. Kindly mention some of the major challenges the company has faced till now.

One of our biggest challenges has been getting initial data on our users and identifying those with the lowest risk. This is partly because traditional scoring mechanisms are either absent or not reflective of the user's true risk. While we initially had the vision of using work data, we had to learn to ingest and process it in a way to create an effective intelligence layer to support our products. This took quite a few partner cycles to attain and we continue to improve on it.

We have also had to find anchor partners with the long-term vision and data hygiene to build a new operations model that requires seamless data integration. While it would have been great if everyone had state-of-the-art API infrastructure, we learned to support all types of automation and manual sharing protocols, while maintaining automation on our platform. Strong data management and data quality thresholds are elusive for many workforce-intensive corporations and this is an ongoing cultural struggle. But hopefully, with solutions like ours, there is a greater imperative for these data cultures to emerge.

Further, we had to validate a new business model that while simple, had no precedence. A subscription-based model for finance is a first in India, and it took us some time to establish its relevance and simplicity for our target segment. Being the first subscription-based credit solution in the country allows us to truly focus on customer lifecycle value.

And then we had to overcome barriers associated with low levels of financial literacy in our target segment, which needed us to design, test, and implement various product features that drove greater adoption and engagement. Real-time loan decisioning to allow users to access funds instantly was one such example. Creating web-based alternatives to mobile apps for frictionless onboarding is another.

These were all moving parts, and getting them together was the biggest challenge. But without addressing these challenges we wouldn't have been able to build the product we have.

4. What is your biggest USP that differentiates the company from competitors?

Our alternative data-driven risk scoring model is unique in the industry wherein we can dynamically underwrite and cover gig & contract workers, who are characterized by real-time risk. This allows us to cover the full spectrum of a Partner's worker base – including gig, contract, and even on-payroll workers – with personalized financial wellness benefits. Further, we expand coverage by starting with very small ticket, short-duration credit products linked to earnings, under a viable financial model, which allows us to get in quickly and offer progressively deeper credit products over time.

5. As you mentioned, tell us how data and AI can be used in financial inclusions

The recent COVID pandemic has disproportionately harmed lower-income segments, thereby depleting savings, creating debt traps, and compounding wealth and income inequalities. In this context, small ticket liquidity is a burning need for these populations and is unfortunately priced very high.

With constrained access to affordable credit, most of our users are comfortable giving informed consent to lenders to gain specific access to their financial or personal data, if it can help them in this regard. We collect user-permissions work and payout data from associated partners, as well as mobile, generated behavioral data types ranging from app usage, device information, call records, location, and movements, and non-personal transactional messages. This helps in establishing and building a credit history from scratch for the new-to-credit or credit invisible population. It is now documented that alternative data obtained from mobile phones can be transformed into 1400+ features that, with high levels of efficiency, can predict useful individual and group level characteristics, a user's personality traits, conversion propensity in marketing campaigns, and socio-economic conditions, including health, education, and standard of living.

But we also understand that with great power comes great responsibility, and it is on us to handle the data safely and in keeping with ethical privacy norms. So we make sure to employ industry best practices when it comes to data security. For example, we never transmit any sensitive user details such as a personal bank account number through any interceptable medium, including calls, texts, or emails, and all stored data is protected through encryption. We do not sell users' personal information or share it with unaffiliated third parties for their advertising or marketing purposes. We employ Virtual Private Cloud on Amazon Web Services(AWS) which provides a secure and scalable technology platform to ensure user services are secure and reliable. We use data replication for data resiliency and backup/restore testing for data reliability. And we will never call users'  to install any remote access software such as TeamViewer, any desk, etc.

6. How do you see the company and the industry in the future ahead?

At a company level, we see an evolving Karmascore that improves prediction efficiency as we validate it across an expanding user base. This will improve our risk outcomes and allow us to make bigger bets. One way to think of it is an ability to underwrite larger tickets and longer-duration loans that meet the segment's deeper needs (e.g., investing in a skilling program, or financing a new vehicle). We will also build a full financial stack to support non-credit financial solutions like savings, insurance, and pension, and evolve towards a neo-banking solution for this specific-needs segment that we understand well.

At the industry level, currently prevailing different models will consolidate into 1-2 winning models to provide sustainable finance to nonsalaried workers, which comprise 85% of the country's workforce. We bet that this will only be achieved sustainably with alternate data that leverages both trends of increased smartphone penetration and digitization of workflows.

7. How does your company strategy facilitate the transformation of an enterprise?

By providing inclusive financial benefits to a large majority of our Partner's workforce (as opposed to only a sliver targeted by most traditional fintech), we can drive deep business outcomes. This starts by providing liquidity to reduce their day-to-day financial stress which has knocked on business benefits in the form of increased worker engagement, productivity, and retention levels. For example, for B2C logistics partners, we have already seen a 20% increase in supply or log-in hours and over 30% reduction in churn levels. Apart from a more aligned and healthier organization, this converts into very significant reductions in cost outlays associated with recruiting, training, and retaining workers.