Powering Growth and Innovation for the Unbanked via Augmented Analytics

Banks and finance houses across the globe have been looking to technology for advantages that can give them a competitive edge for a few years now. While many understand that managed finance services can use technology to increase competitiveness, reduce fraud costs, and allow for more focus on the core business—augmented analytics can have an even larger impact on the finance industry, especially for the unbanked target markets.

Fortune 1,000 companies have been investing heavily in Big Data and artificial intelligence (AI) after watching competitors succeed with these technological advancements in the growing FinTech space. The agility afforded by Big Data, ML, and AI is imperative to survival in a digital environment as businesses continue to evolve with the ability to be more effective with quality data. So now is the right time for companies to leverage this to right-size and address the underserved and unbank consumer market with targeted products and services, a mantra that companies like PayPal have made a top priority.

Augmented Analytics: Big Data Meets Artificial Intelligence

According to industry authorities like Gartner, augmented analytics automates the production of insightful and actionable information. It achieves this by combining machine learning (ML) and natural-language-generation. Another key component of augmented analytics is natural language processing (NLP), which when combined with machine learning, has created powerful pattern recognition tools that help make informed decisions.

The technology’s use of natural-language-generation truly sets it apart as it decrypts information gathered by complex algorithms and transforms it in terms people can understand. The market for business intelligence (BI) has been disrupted by the introduction of Self-Service BI. Now the market is bracing for another major disruption, this time from the rise of augmented analytics and data-driven discovery.

as of 2017 25%* of the U.S. population unbanked or underbanked

Edge and cloud-based big analytics are surfacing as important tools to use in making decisions by companies like Facebook and PayPal who have opened up new markets serving the unbanked. In fact, as of 2017 25%* of the U.S. population unbanked or underbanked, largely due to a general distrust of banking institutions.

Tom Thimot estimates that Gen Z’ers are 40% of all consumers in the U.S, however, by 2020 there will be 75 million millennials in the United States and 65 million Gen Z’ers*. These and other underserved or economically challenged target markets are ripe for leveraging augmented analytics and related metrics for risk management and financial offers.

While there is a whole market out there that’s not being served by the finance industry for lack of information, these analytics tools are helping finance organizations reach niche customers. If you consider the current marketing and technology integration efforts of company like Google, PayPal, and all the major affiliate marketing metrics traction the amount of behavioral data is overflowing.

PayPal infrastructure is currently supporting 2,000 database instances with 74PB of data

Just consider PayPal’s e-commerce penetration—the power of their system is staggering. You can see how easily they analyze your engagement and create highly sophisticated campaigns to target you. Their recent purchase of Simility adds a strong avenue to bolster their fraud prevention solutions and let’s not forget the millions of Venmo users they currently have in their coffers as well. Combining the three is somewhat mind-blowing. The theory is virtually proven when you consider that PayPal infrastructure is currently supporting 2,000 database instances with 74PB of data with the busiest instances handling nearly 1B database calls an hour.*

Augmented analytics is the conjunction of analytics with artificial intelligence. In concept, augmented analytics can produce actionable insight from processing data and sort meaningful information. In this way, it is capable of self-learning and is quickly becoming one of the hottest decision-making tools available to the finance industry.

The most significant advantage of augmented analytics is that it allows organizations to make informed decisions by analyzing their data. These decisions can be made with less manual legwork as the technology is startlingly close to being able to practically think for itself.

What Augmented Analytics Means for the Finance Industry?

The beauty of augmented analytics is that it can figure out how to offer insights from data analysis independently. As such, augmented analytics is powering the finance industry with information that can help streamline the lending process.

Augmented analytics represents one of the single most powerful fintech tools for banking and private label financing. For example, lenders can leverage data on behavioral usage to determine credit line assignments more accurately.

The substantial increases in accuracy that augmented analytics offer mean that banks can take on less risk—better information leads to more informed decisions when lending money or issuing credit. Anyone in finance today who remembers 2008 will readily appreciate the value of reducing risk by using augmented analytics.

How Augmented Analytics Affects Finance Jobs

The analytics-as-a-service market has been experiencing explosive growth as industries like finance have been pioneering the combined use of Big Data and machine learning to make lending decisions.

With firms vesting more trust in decisions based on artificial intelligence, there are grounds for debate on how it will affect the future of finance jobs. The effects on loan officers’ role will be particularly interesting as decision makers in finance start leaning more heavily on the automated insights of augmented analytics.

While the technology will in all likelihood never replace these jobs completely, it will certainly change how they are done. For those worried that augmented analytics is a threat to their jobs, the takeaway here is that it’s not about replacing people, it’s about helping them do their jobs better. In 2017 5%* of jobs were automated, and with the latest advancements, that number has doubled or even tripled. When it comes down to it, augmented analytics is just another tool—one that empowers both lenders and borrowers.

New Tools for Finance Metrics to Pair with Augmented Analytics

Augmented analytics is providing lenders with powerful new metrics and tools to help evaluate applicants. One of the most exciting tools powered by augmented analytics is UltraFICO, which was formed earlier this year.

Millions of people have been barred from receiving credit because of incomplete credit histories with an antiquated FICO score system. Augmented analytics is changing all that. With the metrics used by the latest UltraFICO, a partnership between FICO, Experian, Finicity, finance houses can fill in the blanks on individual credit histories. UltraFICO also examines bank information, meaning that scores will no longer be determined by credit history alone.

Additionally, many larger institutions have their own behavioral and internal cross-channel lending histories that they are leveraging to target the amount of credit that they are willing to risk based on the augmented data.

To test the integration efforts between Experian and Finicity use Experian Boost to supplement your credit report by reporting phone and other utility bills. Finicity will scrub your bank account and pull payments made to such services for the past six months and Experian will, in turn, run it through models to possibly enhance your credit.

However, the science nor modeling behind this is not as straightforward as you may think though. After testing the service with two different Experian profiles with the same checking account, one credit score was immediately increased by 12 points and the second didn’t increase at all. While the augmented data was the same, the parameters around the rest of the credit profile nullified the credit-modeling results for one account.

By applying augmented analytics, someone who would have otherwise been denied a loan for lack of insight on their credit now has a chance to receive financing, or in some cases higher lines of credit for what might appear as incomplete credit histories. For millennials, many of whom have trouble qualifying for financing because of their inherent aversion to credit cards, augmented analytics metrics like UltraFICO can help qualify them as well.

nearly 79 million Americans with credit scores below 680

While the goal isn’t to maliciously target sub-priming lending tactics, the augmented data that can be collected and discerned can help the nearly 79 million Americans with credit scores below 680, along with another 53 million who don’t have enough information in their credit files to generate a credit score.*

Other metrics used in finance that can be enhanced by augmented analytics include:

  • Loan-to-value ratios: Used to analyze the difference in the potential ROI of the loan versus value of available collateral.
  • Retention Metrics: Measures the amount of sustained customer engagement based on the difference between active and inactive accounts.
  • Revenue: Fintech like augmented analytics can greatly improve insights on revenue.
  • Referrals: Analyzes the likelihood of customers who would recommend a firm’s financial services.

In fact, companies like PayPal, Finicity, Simility, Experian and many others have set an example of how using big data, machine learning, and artificial intelligence can revolutionize the finance industry.

The Bottom Line for Augmented Analytics

With so many advantages to offer both lenders and potential borrowers, augmented analytics is going to continue to make waves in the future for the finance industry. The technology is worth investing in to incorporate it into lending decisions and will continue to make much-needed improvements to how these decisions are made.

So, what FinTech companies are you currently working with that are making an impact on unbanked and underserved industries? Share your findings and opinions here and let’s keep the conversation going.

If you’re interested in learning more about leveraging big data for augmented analytics, contact Mel Reyes today.

 

* Erin Barry “25% of US households are either unbanked or underbanked” – 3/9/2019
* Tom Thimot “Why Reducing Customer Friction Does Not Increase Fraud” – 7/6/2019
* Joab Jackson “How LinkedIn, PayPal Each Beat Database Lag with Home-Built Open Source” – 7/8/2019
* McKinsey & Company “A FUTURE THAT WORKS: AUTOMATION, EMPLOYMENT, AND PRODUCTIVITY” – 1/2017
* LaToya Irby “What to Know About UltraFICO: FICO’s New Credit Score System” – 3/14/2019

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