Payments Ecosystem with Intelligent Data

How to Win in the Payments Ecosystem with Intelligent Data

I was honored to speak about intelligent data at the Smarter Faster Payments conference, Nacha’s annual event drawing payments professionals from all over the world. As you can imagine, the audience was very knowledgeable about everything payments related and was especially curious about intelligent data, which resulted in a lively discussion.

I’ll share some of the key points from the discussion with you here since intelligent data is crucial to every industry’s growth.

Many of the platforms and apps that have taken the world by storm depend on intelligent data – like the top streaming music service Spotify. Their platform uses software algorithms to analyze data on what users listen to and recommend songs based on users’ music preferences.

Physical fitness app Strava (Swedish for ‘strive’) is another platform utilizing intelligent data. Strava tracks users’ workouts, letting users see how they’re performing individually and compared to other users.

Google Photos is yet another example, which uses AI-powered techniques to organize photos and make them easier to find. Even if you don’t label your photos, you can still type in specific words to pull them up – like “beach’ to locate that photo of your last vacation.

All three of these apps have built-in intelligence based on data availability, accuracy, and relevancy.

The financial industry is well aware of the importance of data and analytics. Artificial Intelligence (AI) is used by 54% of financial services organizations with 5,000 or more employees. And a full 70% of all financial services firms are using machine learning (ML) to predict cash flow events, fine-tune credit scores, and detect fraud.1

AI is the future, and the reason why is because AI can carry out processes at scale faster and quicker than humans. It can also make inferences that humans miss, by spotting patterns and linking up seemingly disparate sources of information.

The payments industry is a natural fit for AI. New open banking APIs, digital currencies, Central Bank Digital Currencies (CBDCs), Buy Now Pay Later (BNPL), FedNow, high dollar Same Day ACHs, and other paradigm shifts in the payments landscape are paving the way for next-generation financial services.

In addition, the rise of uninterrupted 24x7x365 payment processing is driving the need for better account data, security, and integrity, which is driving the use of AI in payments.

AI and machine learning within the payments ecosystem can:

  • Predict consumer credit card behavior and understand consumer payment preferences and spending habits to drive personalized offers and recommendations
  • Reduce false debit and credit card declines, which cause over $400 billion annually in lost sales when legitimate transactions are flagged as fraud2
  • Correctly identify transaction anomalies with AI-based algorithms instead of rule-based algorithmic techniques that tend to reject non-fraudulent transactions
  • Generate intelligent insights around cash-flow analysis, net worth, budget, expense tracking, and debt management for more informed decisions • Secure payments without compromising ease of use
  • Quickly identify customers making payments with multiple concurrent attributes. Until now, two-factor authentication was the primary solution, as biometrics can be compromised
  • Process large numbers of transactions with low error rates. Both supervised and unsupervised algorithms monitor and analyze these large transactions, look for suspicious activities in user accounts, and send alerts to individuals.

So how do you win within the payments ecosystem with intelligent data? Think RTP – Research, Test, and Prove:

  1. Research new ways to instill computer vision and Natural Language Processing (NLP) techniques
    Sometimes one size fits all, and other times organizations require differentiated products and solutions. So the best way to ensure what works best is to research.

    Some of the most common use cases to get started with are:

    Document Extraction:
    - Digitizing paper-based information
    - Using automation to reduce manual work and optimize the entire document extraction process

    Dispute & Claims Processing:
    - The Chinese FinTech, Ant Financial, uses computer vision to recognize vehicle damage and enable secure claims processing

    Processing Know Your Customer (KYC) Verification:
    - Financial institutions and wealth management firms are using facial recognition scans and other biometrics to authenticate customers’ identities for error-free KYCs and a better customer experience.

    Financial Wellness:
    - Mercantile Bank of Michigan uses its AI-powered MercMoney chatbot to help customers manage their money. Customers can ask everything from “What is the principal?” to “How much do I have left in my grocery budget this month?”
  2. Test & Learn – Mix & Match
    After you build an MVP of your solution and before you consider scaling, invest the time to test and learn, not just once, but on a regular basis.

    Often, mixing intelligent data solutions with other processes can deliver the best outcome. For instance, many financial service providers have found that combining robo-advisors with real-world advisors results in the best customer experience.

    The bottom line is that there is no perfect state, but a continual learning state with intelligent platforms.
  3. Prove with customer experience
    The step that really moves the needle for our industry is when organizations demonstrate and publish their intelligent platform or solution for others to learn from. Along with establishing organizational leadership in the field, this strategy provides a case study for future innovation cycles and advances the growth of intelligent platforms.

    As we’ve seen, popular platforms like Google Photos, Strava, Spotify are leveraging intelligent data to build compelling and innovative customer experiences, and the payments ecosystem has every reason to do the same. Sure, there are no silver bullets, but the RTP strategy of Research, Test, and Prove can help us move forward to build better intelligent platforms.

    The success of the digital economy depends on how soon our industry embraces the changes in consumer behavior and takes advantage of the technologies that are out there. Technologies like machine learning, AI, IoT, and blockchain stand to transform existing business models. We can all be a part of this future.

    One word of caution, though. While Artificial intelligence holds great opportunity for humanity, encompassing everything from Google’s algorithms to self-driving cars to facial recognition software, it’s still in its primitive stages. As late Professor Stephen Hawking warned, AI “will either be the best thing that's ever happened to us or the worst thing. If we're not careful, it may be the last thing.”

So the question to keep in mind is, how can we use intelligent payment data to do things better?

To understand and explore all the ways your company could leverage intelligent data, drop us a line at Envestnet® | Yodlee®.

The information, analysis, and opinions expressed herein are for informational purposes only and represent the views of the authors, not necessarily the views of Envestnet. The statements herein are based upon the opinions of Envestnet and third party sources. Information obtained from third party resources are believed to be reliable but not guaranteed.