Data to inform financial planning, digitally onboard new customers and create a more-holistic financial profile might be the answer, according to members of the Braintrust.
Global Head of Policy at Plaid
If this last year was any indication, the next wave of consumer finance products will need to take a more holistic approach at assessing an individual's financial profile, and that means giving people control over more of their data and taking an API approach to financial access, which allows for more transparency and more control for consumers.
The way creditworthiness is assessed today, which excludes more than 20% of U.S. adults, is arguably outdated and reflects a financial system with institutionalized inequality — from redlining to the exclusion of immigrants. This system needs to be revisited as the fundamentals of the consumer economy shift, compounded by the impact of COVID-19 and financial instability that many face as a result of the pandemic.
Standard questions like "what is your income?" mean different things to different segments of the population, since many people don't live off of a salaried income, some don't have consistent paychecks, others live off of investments instead of cash flow. There is no uniform, industry-standard definition of "income" and how you calculate it. The next layer of data needed to support consumer finance will need to look at very different data sets to get a complete picture of the individual — including things like workforce data, government data (tax, benefits, student loans) and insurance. Assessing an individual's credit worthiness via alternative data sets can remove some of these ethnic and demographic biases within our existing system and put the control of that information in the hands of the consumer. This — along with a shift to APIs so that the ecosystem can have more reliable access to financial data — will help shift the power dynamic that has made credit reporting one of the most convoluted processes for the last decade. And the next decade of financial products will provide us all with greater control and a more holistic view of our end-to-end financial profile.
CEO at Dave
By using transaction data to the benefit of the consumer, fintech revolutionized every aspect of consumer finance from budgeting and saving to investing over the last decade. The new guard challenged the financial industry by using the same data they had access to, but by using it better and with the intention of helping rather than harming their customers.
While new layers of data may become available, the next level of consumer finance innovation will have a similar genesis. In the coming years we will see new machine learning models that disclose not just a customer's current financial snapshot, but also predict and guide their ideal financial situation five and 10 years out.
We can help plan for things like having a family and buying a home — driving innovation by helping consumers along the road to their future state, not just meeting them in the current moment.
Co-founder at Burnmark
2020 has been a revolutionary year in terms of new types of data being collected for consumer finance and banking. A whole new layer of digital onboarding data, from biometrics, dynamic selfies and mobile photo uploads to document photos/scans, have been applied across consumer finance apps and products to aid social distancing. In consumer credit, a whole data layer has been added not just for digital onboarding, but also for digital credit checks using social media data (from follower counts to keywords and hashtags used).
The interesting thing is that consumers are now hyper-aware of how their data is being used for these finance-related purposes, and seem to actively encourage 'trading in' data for financial benefits. 2021 will be a very interesting year to look forward to, for the mass adoption of these use cases.
Chief Analytics Officer at FICO
It depends on what consumers want their financial products to accomplish. I believe consumers increasingly want to be empowered with a constant stream of individualized information to help them make better financial decisions.
With a nod to the efficacy of Open Banking initiatives everywhere, an abundant amount of detailed transaction data already exists to support that goal — but customers' ability to engage with that data requires their explicit consent. Banks' ability to obtain and manage specific customer consents will directly impact institutions' ability to create that "next layer" of transformational data.
When customers consent to make their personal data available, machine learning models can consume transaction information in real-time, spawning an exciting array of incremental features to enable new tradelines, savings on purchases, proper and consistent saving and investing, and more – for example, machine learning and AI can help consumers get their credit under control by advising which specific purchases to make, to postpone or to avoid altogether.
To be truly transformative, new data-driven features must be highly accurate and offer personalized insights. Those that don't will get a lukewarm consumer reception at best. Thus, streaming machine learning models fueled by comprehensive real-time transaction data are the key to delivering the holy grail of digital banking, a "personalized AI banker" that can react to changes in customer behavior, keep customers motivated to stay on a budget and help them move more nimbly to achieve their financial and life objectives.
Chief Innovation Officer of Citi, Head of Citi Ventures & Head of Citi Productivity
While today's computational tools, like next-gen processors and cloud computing, are making it easier for us to make sense of vast quantities of data, the next challenge will be how we hone in on data that enables people to make the most effective decisions for themselves and their families. For instance, we already have a lot of data, metrics and signals that measure the health of the economy. To transform people's lives, including through consumer finance products, we don't need more data but rather the ability to use the data we have for actionable, real-time, in situ insights.
One example we have worked on is repurposing job description data to help people better understand the worth of their personal skillset and give people new tools and resources to increase their earning potential through new skills or career opportunities. Just under a year ago we launched Worthi by Citi, a free, skills development-focused tool that helps workers identify which skills are in demand and connects users with online courses to further their career path.
Traditional finance products often focus on managing capital, not investing in yourself to increase your capital pool. If we leverage data more intelligently for situation-specific insights that address personal, hyper-local challenges with precision, we have an opportunity to improve people's lives while also driving overall economic vitality.
See who's who in Protocol's Braintrust (updated Jan. 6, 2020).
Questions, comments or suggestions? Email firstname.lastname@example.org.
Kevin McAllister ( @k__mcallister) is a Research Editor at Protocol, leading the development of Braintrust. Prior to joining the team, he was a rankings data reporter at The Wall Street Journal, where he oversaw structured data projects for the Journal's strategy team.
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