By Debleena Majumdar
Head of Branding and Communication Strategy
Menterra Ventures



Disrupting with data – the birth of Fintech:

It was the aftermath of the 2008 crisis. To their increasing horror, people saw their retirement assets getting depleted and their investments failing to provide the alpha returns they expected. The confidence in the market was low and need for innovation was high. Like many strategy teams, we were busy studying emerging trends. And we stumbled upon the company called “Motif Investing” that seemed to be approaching investment in a very unique way. Motif used data innovatively to create a model of theme based investing wherein common people could identify themes from everyday life such as “battling cancer” or “tablet takeover”. One could create or buy motifs featuring up to 30 stocks or exchange traded funds weighted to reflect an investment theme, market insight or innovative trend. And control the weight of stocks within a motif till they reached the desired portfolio. Apart from the price advantage, the most interesting thing about the model was how data was used to democratize and automate the investment process.

In the years since then of course, this alternative data based approach to traditional mutual fund business has got its own catchy name – “Robo Advisors” and has shown enormous growth with the market expected to touch $2T in AUM by 2020.

Including Data in Financial Inclusion:

The buzz around Fintech has never been stronger than now. But among the dizzying reports of Fintech growth, innovation and funding, the question that sometimes doesn’t get raised is – can there be more such Motifs which can use data innovatively eradicate the multi-headed monster called Financial Exclusion? Focus on cost-efficiency and personalized customer services can help those already in the financial system but the bigger need for emerging economies is bringing about financial inclusion for the unbanked and the underbanked. And the key issues remain lack of credit history, a very diverse profile of customers and poor connectivity. Despite multiple efforts to drive financial inclusion, the fact remains, banks have not been able to cover a significant portion of the population in India giving the opportunity to Fintech companies to use data innovatively to help banks in addressing the issue of lack of credit history. And leaving connectivity issues to God and government, there are a more than a few companies that are trying to address the lack of credit history with innovative data approaches. A few such data driven applications include using Aadhar enabled KYC and access to digital identify, transfer and payments in remote areas, e-wallets and new credit scoring models to drive alternative lending to consumers and small business.

An alternate reality?

Of the areas mentioned above, a big area where data can really make a difference in the world of Financial Inclusion is actually Alternative Lending. According to Tracxn report on Alternative Lending (June 2016), the alternative lending sector in India currently has over 150 companies. Across consumer and commercial loans, there are multiple sub-segments within the alternative lending landscape. And there are enablers that provide comparisons and ratings. While some companies focus on on-book lending, a few are structured as marketplaces.


Crediting data for what it’s worth:

Beyond the credit bureau data that historically drove credit scoring and underwriting rules, what lies at the heart of Alternative lending companies is their credit scoring algorithms that include alternative data to score credit worthiness of those who do not have bank accounts. Such alternative data sources could include browsing patterns, device location for fraud detection, social media, telephone records (pre-paid top ups, bill payments), education marks etc. Traditional modelling techniques get challenged in this scenario with need for newer methods such as artificial intelligence, survival modelling and neural networks for social media analysis.

Can such models really expand access to credit to individuals and small businesses and beat existing underwriting processes? Let’s look at two examples:

Alternate data stories:

  1. Company: Lenddo, Hong Kong

Status: rolled out social media credit assessment across multiple geographies such as Philippines, Colombia and Brazil

Alternative data used: social media behaviour

How the model works: Using a proprietary algorithm, the company rates borrowers on a scale of 1 to 1,000 based on their likelihood to repay a loan. Scoring is done on the basis of thousands of data points gathered from social media activity across multiple platforms including number of social media accounts linked to the customer’s Lenddo profile, number of friends and followers, length of active social media time, strength of social network.

  1. Company: Vodacom, mobile service provider in Tanzania

Status: Partnered with data analytics company, First Access to score the customers

Data used: mobile records

How the model works: First Access offers an instant risk scoring tool for low-income customers. Scores are authorised by subscribers via text message and delivered to participating financial institutions real time, along with a recommendation on the loan size in the local currency, and eligibility for instant disbursal.

A bankable future or a bigger bubble ahead?

The benefits of alternative lending are quite obvious in the short term. But as the trend starts moving from alternate to mainstream reality, a few risks become critical to address:

-Validity of the models and the data points: Unless there is enough data to test the models, the efficacy of the models remain questionable vs. traditional scoring models.

-On-book lending vs. marketplace models: According to people currently working in the industry in India, such loans currently face more than a 60-70% rejection rate. Does that make on-book lending an easier option rather than the peer to peer or B2C marketplace models?

-Investor protection: Compensation and guarantee schemes could be limited leading to low capacity to absorb defaults.

-Data protection: How much of personal data becomes authorized to be used for credit scoring is another grey area yet to be solved

However, there is a bigger question that remains unanswered. Can such loans, and the stretching of credit terms and pricing issues again lead to a bubble similar what triggered the 2008 financial crisis? That would be a vicious cycle indeed – if the very crisis that caused people to think of innovative applications of data leading to the birth of Fintech, could yet spur another one.

Debleena recently became Head of Branding and Communication Strategy at Menterra Ventures, an organization that invests in companies and entrepreneurs driving real change in education, healthcare and agriculture. The story of the firm is the story of these change makers. Stories Debleenawill be helping to shape and tell.

 Debleena alsowrites stories about Strategy and Analytics.  Blog:, Twitter: DebleenaR