We often get asked, “What does cashflow for a person mean?” This is well understood for businesses — a restaurant knows how much they need to spend on rent, raw materials, staff, insurance, etc. and their inflows come from their customers and tips.
For an individual, it’s a lot murkier. Some basic questions:
- Does a rent check from a shared apartment count as income?
- What if the individual owns the apartment?
- How many expenses are regular or expected like utility bills vs. one-time expenses like an emergency doctor’s visit?
All of this context is buried across many different systems and thousands of row-level metadata that’s usually very messy, fragmented, and hard to analyze.
Let’s take a look at a specific example of how analyzing the cashflow of an individual comes into play.
Maya, a 40 year old Registered Nurse living in St. Louis Missouri, was previously banking with a traditional financial institution that offered her the same cookie-cutter products as everybody else. Her bank did not contextualize the details of her financial life and as a consequence, she was not offered the best financial services that met her needs.
This was the impetus for her to explore fintech solutions that take into account the context around her cashflow and financial profile. In the example below we can see that her data is sitting across many different systems that each hold unique information about her financial situation.
As a nurse, Maya’s work hours are in flux. She gets a direct deposit from her employer every two weeks, but the amount fluctuates. The fluctuation makes it difficult for her to forecast how much she can spend between pay periods.
Like most other nurses, she makes payments towards her nursing school debt, auto loan, and credit cards.
The fintech apps she uses are a part of her daily livelihood.
- Maya was swimming in credit card debt after an emergency car repair set her back on her payments. Her friend pointed her to Plannery, a credit app specifically designed for nurses and physicians. Plannery understands that a nurse’s job security is almost guaranteed due to the nursing shortage, and is able to offer nurses a debt consolidation option that is uniquely catered to nurses.
- Maya uses a digital bank called Lume which is also designed for nurses and offers special discounts and early payments.
- She manages her student loan payments on Lume via Payitoff, which also helps her see if she qualifies for reduced payments
- Maya connected her bank data via Plaid and Ocrolus to Monarch Money, a personal financial management app that helps her track her spending, savings and investments
- Like over 60% of US households, Maya has a pet and is a Gofetch subscriber for regular vet care. When her puppy got sick, Gofetch issued a virtual card to split her vet bill into installments.
The above is still only a fraction of the complexity of Maya’s financial life.
Why this matters
So why is there so much value in being able to get a complete picture of Maya’s financial profile?
Consumers today expect instant wages, transfers, liquidity, crypto/fiat conversion, and credit decisions.
Like many consumers, Maya expects speed, convenience, and personalized offers for financial services when she needs them. She expects that apps will use the information she’s trusted them with to provide personalized and instant financial services like a personalized financial plan optimized for her situation, and automated money movement between accounts.
All of these expectations require each fintech app she’s using to have a detailed understanding of Maya’s financial life.
The cost of acquisition for financial services is high
Acquisition costs are one of the biggest challenges for any lender. Today, companies spend millions of dollars buying targeted lists from the bureaus in order to identify people with an inclination to take on new credit.
Meanwhile, these companies are sitting on treasure troves of financial data that may contain signals around their customer’s willingness and ability to pay. However, they struggle to leverage this data due to difficulties in data cleaning and analysis in order to generate customers’ insights.
A growing number of consumers are underserved by FICO and traditional sources
The demographics of our country are changing. We have a growing immigrant population, more people have multiple sources of income fueled by the creator and gig economy, a divergence from traditional investing to crypto, minority groups a younger generation with inherent distrust in the traditional credit system. These consumers are left out, and a rapidly emerging fintech ecosystem is reinventing how new products are designed for underserved consumers.
How does Pave solve this?
For every company building a financial service, it’s a major effort to get a complete and accurate understanding of their customers’ financial lives. Rather than recreate the wheel, Pave is building a solution to standardize how everyone views the cashflow and financial profile of a consumer.
We’re making it possible for developers and data scientists to connect all of their disparate data sources to Pave, and get back a unified view of the end consumer’s financial life in order to provide increased access to credit and personalized financial journeys.
Contact us here to learn more about how you can use Pave’s cashflow insights.
