Data is power. But is it powerful enough to resurrect personal financial management tools?

A few years back, everyone who’s anyone in the financial world seemed to lament the end of personal financial management tools. With good reason, too: what started out as a revolutionary means to make people’s lives easier and their finances healthier turned into a lacklustre affair. The most widely cited reason for slow adoption was the tools’ fussy and complicated functionality. The advent of artificial intelligence and machine learning, however, has opened up new horizons in data analytics. Is it possible that they will also bring about a revolution in personal money management?

Against all odds, the answer is a resounding yes. PFM has been around for some three decades and has actually come a long way in recent years. But customers looking for help with money management didn’t exactly jump on the possibilities that the first PFM tools offered. Or they did but then struggled to make sense of their money management app and quickly abandoned it. Or, worst of all, didn’t even know it existed. A 2017 study by Celent estimates that the share of PFM users plateaud at a mere 10-12% and several PFM solutions, such as Level Money, Prosper Daily or Zenbanx, were axed in the same year.

But that doesn’t mean the whole concept should.

A new approach to money management

The main reason why PFM deserves a second chance is that the demand is clearly there. “Millennials will inherit the largest amount of personal wealth of any generation,” CB Insights pointed out earlier this year and new personal finance apps are going in for the kill. 

According to the market intelligence firm’s estimation, 92 million of these big spenders will soon approach their “prime spending years”, as Goldman Sachs would say. And rack up $1.3 trillion in annual spending while they’re at it. Yet, as a survey by MyBankTracker has revealed, 53% of millennials invest exactly zero time in managing their personal finances.

It’s not that they don’t want to though. Take young people who are less experienced with money, for example. Two thirds of university students with a maintenance loan say debt would be less stressful if their bank offered a digital money management tool, according to a survey by UK software firm Intelligent Environments. Millennials may be considered budget-conscious but a staggering 49% of those surveyed by US lender Revere Bank use offline methods, like a spreadsheet, when budgeting for future expenses, while 36% has no determined method of budgeting. 

Can digital money management tools fill this void?

Certainly, but not without a complete makeover. Traditional PFM tools did indeed help customers manage their personal finances. But it was still the customer who had to do all the legwork and make all the decisions. And frankly, who can – or wants to – define a budget for themselves? And how many people will set up saving goals manually? That would take a lot of effort and thinking. Money management tools should require less work from millennials or any other age group, for that matter. In other words: it shouldn’t be the customer who’s doing the actual management. And this is where data analytics enters the picture.

New update available?

“Banks must be able to proactively address their customers’ money management issues and offer them tailored solutions, making use of the wealth of existing customer data they have,” says Gellért Vinnai, product manager at W.UP. Data-driven money management can help clients handle life events, big and small, and offer much-needed guidance on spending, budgeting and setting saving goals. Functions should include tracking net wealth and giving personal finance tips based on peer comparisons. And needless to say, it should be up to the system, not the customer, to detect any changes in the user’s personal finances.

The ideal money management system sends alerts to customers when, for example, unplanned costs put making ends meet at risk or savings may run out soon. Based on banking data showing their regular transactions, customers can also be warned when they reach their monthly budget limit because they have eaten out too often, used Uber too much or simply spent more than usual. “The next generation of money management tools must also be able to identify periods when customers tend to spend more, for example, during the summer holidays or on ski trips, before birthdays or Christmas,” Vinnai suggests. 

PFM: Get alerts when needed

Using advanced data analytics, banks can also define long-term savings goals for customers, like buying their first car or home or putting money aside for retirement, based on their profile and spending habits. “The point is that the system must detect or recognise the life situation when these functions could be of interest or useful to customers, and free them from having to set up, track or look up these functions themselves,” Vinnai says. According to the W.UP expert, these alerts will be the backbone of the intelligent money management services of the future. “Do we really need PFM screens, functions and charts? Isn’t it more efficient if the system simply warns me when needed and then takes care of everything for me?” he adds.

And that’s only the tip of the iceberg. Barclays, for instance, might be one of the oldest banks in the world but it has also been a pioneer in rolling out budgeting tools that make customers’ lives cushier. It first added spending control features to its mobile apps for key spending categories, such as phone, groceries, restaurants, bars and petrol. In late 2018, they turned it up a notch and became the first UK high street bank to allow customers to ‘turn off’ entire spending categories with a tap of a finger. While the feature comes in handy for any budget-conscious customer, blocking payments to retailers such as gambling services and premium rate phone lines can be a life-changer for those battling serious mental health issues, addiction or debt. 

This post was originally published on 18 December, 2017 and has been updated to include recent developments.

44 USE CASES FOR BANKS TO BOOST ENGAGEMENT AND DIGITAL OFFERS

44 USE CASES FOR BANKS TO BOOST ENGAGEMENT AND DIGITAL OFFERS

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