Getting People To Use PFM

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What would it take to make personal financial management apps more popular? Cash flow predictions and deeper spending insights, say many observers in this market, from entrepreneurs to analysts to bankers.

"Historically, most PFM solutions focus on what has already occurred. It's too late. It's done. You spent it. It's gone," Jacob Jegher, senior analyst at Celent, tells BTN. "Getting rid of the concept of what has taken place and focusing on the 'here today' and 'where to tomorrow' is a far more interesting tool."

Though such forecasting tools are particularly valuable for small business customers, Jegher views them as relevant to consumers. "It could be a silver lining to PFM," he says.

The broad idea of these tools, still mostly invisible to many consumers, is to provide visual estimates of account balances for a certain period of time ahead based on the user's calendar data like upcoming bills. Consumers, in theory, can use the tools to help answer queries like: am I on track to afford a house in seven years? Or, will I fail to pay rent if I buy this Nanette Lepore dress?

Sensing the consumer need for spend management advice, companies have wanted to find ways to deliver the technology for years, especially to populations like the underbanked, which accounts for 20.1% of the U.S. households, according to research released by the Federal Deposit Insurance Corporation in September of 2012. The FDIC defines the underbanked as households with a bank account and user of alternative financial services.

The holdups, however, have been many for a risk-averse industry: Compliance hassles, corporate red tape, design kinks, and the need for the data's integrity to improve and transactions to post quicker are just a few. But a cadre of fintech players, including banks, have been working through the obstacles by running software tests and trials. Some have quietly pushed out newer services in recent months.

Consumer-facing software from Planwise already offers ways to visualize estimates of one's financial future while Simple provides its users more insights about their transactions. Meanwhile, Banno and Strands are among the companies that have sensed this developing niche and are thus designing and offering pre-transaction estimating tools to banks in recent months.

The sophistication of the modeling varies by vendor.

Planwise takes a simplistic, calculator-like approach. In its tablet apps and website, it depicts a consumer's future cash flow as a line graph after the consumer provides estimates of expenses, debt, income and goals. Banno aggregates data sources from bank-linked accounts and ties that information, among other sources, to a person's location for display in its mobile app Grip.

"People have been talking about [the technology] for a couple of years," says Jim Bruene, editor and founder of The Finovate Group and the Netbanker blog. "The more exotic and helpful ones anticipate [the customer's] spending and project it forward. There will be different shades."

The models vary by player, as do the names: "safe to spend," "help me decide," "future plans" and "what if?" are a few to wit. But the objective of the software is the same: to simplify the mental math behind purchases - to buy a polka-dot sofa or not to buy a polka-dot sofa -- in any given moment.

Though startups are leading the way in debuting such tools, banks are brewing up their own concoctions too, albeit at a slower rate.

Wells Fargo & Co., the fourth-largest bank in the U.S. by assets, has been working for months on tying a 30-days-out balance estimating tool into its online banking offers, with plans to launch the feature sometime this quarter. Meanwhile, Cleveland, Ohio-based KeyBank rolled out an app that included a balance forecasting feature in late 2012. However, that service was no longer available as of mid-January.

"In the course of introducing myControl to KeyBank online banking customers, we obtained valuable feedback on ways to refine myControl and we are in the process of incorporating those refinements," said a KeyBank spokesperson in an email to BTN.

Despite such early entrants, forecasting functionality seems likely years away from entering most mainstream banking apps. "It won't take off that fast," Breune says. That said, "If BofA does it in March, that's a whole different story."

Even for third-parties like the poster boy of PFM, the challenges are numerous.

Mint wants to offer customers forward-looking features but has yet to do so. "We are interested in it, but it's one of our top challenges," Vince Maniago, group product manager for at Intuit, tells BTN.

Several of company's users have been vocalizing their desire for such a tool for years in public message boards.

"However, what a disappointment to find out that there is no cash flow forecasting available. Such a great tool is rendered completely useless to a lot of users due to one very important missing feature," wrote AngieB in 2011.

Though Maniago hints at the possibility of "seeing" how Mint could do more with financial forecasting -- Quicken, an Intuit product, has had various forecasting tools in place over the years -- it first must answer this question: How's a software provider to inspire a consumer to work in order for the model to make better assumptions?

"It's not as easy to figure out when [people are] planning to buy a new car unless they are sharing those plans," Maniago says. "It puts the onus back on the user to talk about upcoming events."

Beyond goals, consumers must cough up data like their bill payments and income sources to the software. Though aggregating all of one's financial accounts is one way to help achieve this, even then, the software may not accurately identify the transactions.

"There are all kinds of issues to what is being put into the forecast," Celent's Jegher says. "The worst thing a financial institution or third-party vendor could do is present something that's not relevant and eats up real estate and consumers turn it off."

Consider how many sources contribute to a person's earnings, for example. Say a consumer has a set salary and receives an expense report payment in a given month; the software must properly identify the income nuance or the user must tag it correctly so as not to upset the forecast, illustrates Jegher.

"Data quality is a hurdle," he says. "How do you minimize the manual labor while providing predictive cash flow that helps consumers make a decision?"

That's hard. People who are "flying by the seat of their pants" are unlikely to rush to shift their behavior to start looking ahead, while type A personalities are likely to already engage in their own modeling. The wealthy might not care, unless the data forecasts their investment performance rather than future grocery spending, Jegher suggests.

"It must be simple, quick and almost fun and interactive for a user to do this," he says. "It's very challenging to do."

Historically, getting people to do their financial homework is like trying to score dates with Brad Pitt: Expect to strike out.

Indeed, banks already offering PFM features through online banking have failed to capture most of their customers' involvement. Indeed, active use of PFM services within online banking hovers in the single- digits, according to Celent research published in 2011. The reasons are many, including poor auto categorization, ugly UIs, a vague definition of the category altogether and a consumer behavioral problem. Despite the lack of enthusiasm to date, the firm forecasts PFM adoption to grow in the years to come, especially if banks refine their PFM products to include features like forward-looking tools, among other improvements.



Though companies are billing their models as ways to help consumers become quickly informed about their financial states before they buy something whimsical, skeptics point out the need for consumers to stay tuned to their service providers' business motives.

Cathy O'Neil, who earned a Ph.D. in mathematics and is co-authoring a book called "Doing Data Science" with Rachel Schutt, senior statistician at Google Research and an adjunct assistant professor in Columbia's Statistics Department, says the primary issue with modeling is that the results are uncertain. "The overwhelming thing to keep in mind is that [modeling] is a shot in the dark," says O'Neil, who also pens the blog mathbabe, which explores quantitative issues.

Another issue from modeling is what is known as a feedback loop, which occurs when a model gives a person the illusion of control; thus, potentially making the data a self-fulfilling prophecy. "It doesn't mean the models are right, but they become more right if they've engendered trust," says O'Neil, adding whether that's good or bad is somewhat subjective.

Then, picture a person who has been dealing with his finances for 40 years and one day decides to sleuth out short-term cash flow concerns through a model. Perhaps the concerns highlighted by an algorithm never occurred to him previously, she explains.

In turn, the possibility of "worst-case scenarios" might inspire him to buy insurance to "avoid" the risks, which then adds new concerns as well as increases the person's dependence on a model, O'Neil argues. In other words: Be wary of a model that also sells insurance as it has a vested interest to scare "the s*** out of you," she says.

Meanwhile, for account aggregation services that help some people manage their money, there are other business motivations users should be aware of, adds O'Neil: When people provide their transaction data to a third-party, they might risk predatory marketing. Say someone has an addiction to gambling and offers their transaction data up to a company. "Know you are vulnerable to certain deals," she cautions.

Her point underscores how companies' reasons for offering spending insights will vary.

"Predictive spending patterns have different usage," says Director of Product, Data and Partnerships Mohamed Khalil at Movenbank, a financial services startup that has yet to launch. "It might not be financial wellness as much as to make sure a [customer] has enough funds to pay on time. Everyone has their own reason to do it. ...It's an emerging area with a lot of experimenting."

A case in point is Movenbank's ambitions. "We want to be sophisticated like a Netflix," says Khalil. "We don't want to be Big Brother but want to provide context. ...Why use a product that gives no insight?"

This idea, already used by online retailers, is in its early stages for financial services players looking to provide trending information about consumer's total spend, says Khalil.

To that end, Movenbank plans to initially offer experiences that get people to pause while spending. How? By showing them data nuggets at point of sale like how their general spending on Tuesday compares to previous Tuesdays and how much more they can safely spend, for example.

"We're focused on financial wellness," Khalil says.

The market is ripe for such innovation, according to Khalil, for these reasons: better aggregation capabilities, maturing mobile technology, transactions posting quicker, and recession-weary consumers and unemployed recent college graduates lusting for better spend management guidance.

Ways to innovate the transaction stream are catching the attention of many.

Bradley Leimer, vice president of online and mobile strategy at Richmond, Calif.-based Mechanics Bank, expects advancements that will encourage customers to better engage with their transaction flow, by way of the smartphone's camera and GPS, to blend in with financial forecasts.

"Imagery and social engagements need to be woven in," says Leimer, adding the intent of such efforts is to personalize a person's transactions into an almost mini-diary experience for her. He also envisions a digital banking world that will include contextual offers and gamification features displayed within the mobile transaction stream as ways to impact a person's decisions on fanciful purchases.

Key to all such efforts coming from PFM providers is to avoid pushing consumers past their budgets via tempting offers when their original missions centered on helping users manage money. "They need to be proximity aware and also transactionally aware," Leimer says.




Forward-looking cashflow analyses are a key to growing adoption of personal financial management tools.

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