What does Machine Learning Have in Store for Inclusive Fintech Startups?

Authored by: Matt Grasser
February 3, 2017 - 8 mins read

How Destacame.cl Uses Predictive Algorithms to Serve Underbanked Customers


What is “ML” and Why Should I Care?

In 1959, Arthur Samuel, a pioneer in the field of machine learning and artificial intelligence during an era when computers filled an entire building, defined Machine Learning (“ML”) as:


While Arthur Samuel was busy defining Machine Learning, ENIAC, the first modern (“Turing-complete”) computer, pictured here, circa 1947, was being heralded as a “Giant Brain” by the press.

During a recent keynote, Microsoft CEO Satya Nadella referred to data used in this context as “the new electricity,” calling our current era a “fourth industrial revolution” following steam, electricity, and digital technology. Scott Guthrie, EVP, also acknowledged that data is “enabling every business to be the disrupters of their industry by harnessing the power to drive insight from this data.”


The adopters of these tools have typically been rewarded for their efforts with more engaging, targeted, and useful product features, reduced costs, and increased revenue. In short, machine learning, when properly applied, has the ability to provide a significant competitive edge for an evidence-based outfit.


A sampling of cloud-based providers of Machine Learning as a Service (MLaaS) and select use cases (inspired by this deck)

Destacame.cl: A Data-Driven Fintech Startup

A fintech company that is embracing this ML-driven future with open arms is Destacame.cl. The Catalyst Fund (CF) team has been working with Destacame.cl to learn how ML could improve the way Destacame.cl identifies good and bad payers based on a score.

Based in Provedencia, Chile, Destácame.cl is a fintech startup focused on helping unbanked individuals gain better access to mainstream financial services via the digitization of alternative data sources like utility bills, automotive credit history, census data, airtime payments, and more. These data serve, not only to provide a proxy to the recommendations of traditional credit scoring models, but also to complement current credit scoring algorithms. In that sense, Destacame.cl allows individuals to earn access to small lines of credit by essentially becoming a “Credit Bureau of One.”

The current scoring process starts with a user who is looking to get better access to credit. The user signs in into Destacame’s platform, and identifies him or herself, and by using some numbers that are specific to domestic, the user can automatically upload his or her payment history to Destacame.cl. This is possible because Destacame.cl has a connection to almost every utility company in Chile. These data points, combined other data sets are used to seed the proprietary algorithms (reflecting the most statistically significant indicators) that power the predictive credit-profiling model. The output of these algorithms is the Destacame’s Score that rates the user’s likelihood to repay a loan in full to a lending bank.

The score represent how responsible the user has been with their payments in the past 12 months, clarified Sebastián Ugarte, CEO of Destacame.cl.

Additionally, Destacame.cl allows their users to access their credit report for free, so people can check out their financial health, and understand their chances of getting a loan.

The score and the credit reports are then used to determine which financial offerings can be unlocked and accessed via Destacame.cl’s website. This web application also provides active guidance to individuals on how to build their score. For instance by connecting additional data sources, building better habits, and more, in order to gain access to even better product offerings.


Basic sketch of the scoring process: using historical data (labeled as “DATA” here) to train a model, which then can use new incoming data (labeled in the blue circle as “I” here) to predict results.

The Hypothesis — What We Believed

With all these great features, there were still some areas within Destacame.cl’s processes that had not yet been optimized. When we first met, Destacame.cl was using hand-made statistical models and had not yet fully incorporated Machine Learning into their score.


Enhancing the Product with Machine Learning

Our goal as CF was to enhance Destacame.cl’s ability to more precisely predict who will default based on individuals’ historical financial behavior. Statistically speaking, making this improvement meant that we wanted to implement a ML algorithm that would:

Destácame.cl’s Sebastián Ugarte and Macarena Silva, strategizing with BFA’s Ashirul Amin and Matt Grasser in NYC around an enhanced data-driven credit scoring process.

The Process – What We Did


  1. Replicate Destacame.cl’s proprietary existing statistical model’s results, examine any inherent assumptions, and assess what improvements can be made.
  3. Translate from abstract improvements in the statistical model to concrete revenue increases in the business model (% → $) via a Return on Investment (ROI) model. The idea here is to take the predicted customer behavior and turn it into an expected net revenue for the product within the financial institution.

The Results — What We Learned




Choosing & Training ML Models



Step 2: Decision Trees. Building on logit as a ML baseline, we decided to explore a number of tree-based machine learning techniques as well, namely: Regression Trees, Conditional Inference Trees, Evolutionary Trees, and Random Forests. Each was chosen for its relative strengths.

Overall, we saw comparable results, with Conditional Inference Trees providing a slight improvement in sensitivity and Type II error, at the expense of increased Type 1 error:

Simply, this means that these trees are better than logit and therefore also the initial model at predicting delinquencies, but will turn away more good loans than logit as a result.


One thing is clear here regardless of the final model choice between logit and conditional inference trees: machine learning has produced substantially better results than the already successful existing model, for everyone involved.

A highly-exaggerated example of before & after a properly-trained machine learning model is introduced.

Takeaways for Our Learning Agenda

1. With Machine Learning, Great Power Means Great Responsibility.
ML is a powerful tool that allows us to extract intelligence above and beyond what other approaches like manually generated classifications, can do. However, ML is not a silver bullet and does require some careful consideration prior to a final choice of model.

For this analysis specifically, we saw a case where improving our Sensitivity meant increasing Type I Error. Some institutions will happily accept that tradeoff, while others would be happy with logit’s improvements over the existing scoring methods. The only way to choose which model is best is to understand the context in which it will be used, and build a financial model that makes the criteria for that decision explicit.


2. This is a Constantly Evolving Field.

3. Follow the Data.
By paying proper attention to any of the data we generate, and training machine learning models to recognize significant patterns, we are effectively creating feedback mechanisms that allow for incremental improvements across any imaginable range of cases. Perhaps the kinds of data sets we work with and the refining of to the ML algorithms for the low income customers in emerging markets has implications for reverse innovation for mainstream fintech and other data-heavy and customer-centric industries in established markets.

4. Steal this Framework! (but remember the devil is in the detail)

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