Transactional data is generated every time money is transferred between two entities, one initiating the transaction order and the other receiving it. In the previous post we described the complexity of extracting information hidden in transaction data. However, if done right, the use cases are endless.
According to Mastercard’s recent report, over 9 in 10 people in America use technology to manage their money. These are mostly digital products to complete simple financial tasks such as paying your bills, banking and so on. However, the need for more sophisticated solutions is becoming clear, especially among late millennials and GenZ-s. Various forms of financial forecasting, automated investing, flexible savings accounts, crowdfunding and crypto integration are some examples. At the core of all these possibilities lies the ability to extract insights from transaction data in an automated way.
Here are a few emerging use cases that we love the most:
Where and how you spend your money directly affects the planet. To be able to change, we need to keep track of, and understand our spending habits.
High-resolution transaction data will play an important role in building a carbon negative future. There is a whole generation of companies, such as Ikea, Henkel, Brewdog and Microsoft committing to this.
A few decades ago, as an entrepreneur building something from scratch, you had to have great “friends and family” to access capital. Later on you had to look and act the part to gain the trust of your bank manager.
Now, you need to have years of experience being in debt, a credit score and fill out a bunch of paperwork. The experience is a pain. With a bit of luck, you may get the capital you need, but not on the terms or timelines that you would like.
Good quality transaction data can also provide a detailed view of the present and possibly the future state of a business. If processed by a machine-learning model, it can reduce the time to access capital from weeks to seconds. Indeed, transactions can be pulled from the bank accounts of your customers in seconds, sent through an enrichment engine (e.g. Ntropy API) and fed into your underwriting pipeline for a final decision.
Imagine you are the operating system, aka the CRM, the payment processor, the employee management system or the customer comms layer for car repair shops in the US.
You have saved them from the manual pen and paper operations and alleviated many of the operational pains associated with managing their business. Your software environment is technically the home for their business. However, when they want to grow, make changes, hire more, invest in inventory or get started, they need to go to a bank. This is an entity who knows nothing about them or their business and who has to start from scratch to understand their case. Banks spend large amounts of time and money to assess the credibility of applicants for their financial services and later charge that back to their customers in onboarding costs, communication and interest rates.
Instead, vertically integrating into the business of your customers, you can allow them to connect their cash flow and financial information without leaving your premises. You get very happy customers and 3–5x LTV. They get time and resources to run their business instead of messing about with banks.
In order to make this happen, banking data needs to be machine-readable at scale and easily married to other meta-data about a business, including CRM information, customer service data and reviews.
As much as we hate the word “hyper”, getting things that you actually care for vs generic discounts and points is fun and convenient. Adoption of new financial experiences is generally driven by two factors: accessibility and convenience.
Fine-tuned rewards and recommendations available at the fingertips of your customers are excellent for boosting their loyalty, satisfaction and product experience. To do this, you need to know where they spend their money and time. That information is, you guessed it, hidden in their transaction data.
Automated business expense management and personal finance management have so far been the primary use case for transaction data.
In order to allow your users, whether a business or an individual, to have an overview of how to make their money last, you need a granular understanding of where it is coming from and where it is going.
Traditional money management contains a lot of friction. It is retroactive, time consuming and inefficient. It works for people and companies who are ready to go the extra mile and put a lot of effort in making it work. For the rest of us, most of these tools fail. Hence, a hotbed of new opportunities for fintechs emerges.
However, as most fintechs, like a Truebill or a Ramp are moving towards empowering their users with a more holistic suite of money experiences, diverting focus on trying to build a transaction categorization engine in-house is not a poor strategy. The effort is extensive, the cost of mistakes is high and the risk of ending up with a suboptimal solution is apparent. For example, tagging data labeling as rideshare or restaurant costs as online education can result in immediate churn of a customer.
If you want your suggestions to be reliable, you need high quality and scalable transaction tagging and real-time notification for users to take action.
Finally, one of the biggest emerging use cases for interpreting transaction histories is within the creator economy and identifying potential high earners in order to fund and scale their businesses. We will elaborate on that in our next writeup.