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The Anatomy of a New-Gen Personal Finance Management Application

By Anna Oleksiuk – FinTech Industry Leader at Intellias

Where is personal financial management heading? How the future of autonomous finance will look? This comprehensive overview has all the answers.

Money is a complex matter. People always want to figure out how to pay off their student loans, save for a house, or plan for retirement. And to achieve any of these goals, they may wish to have wealth management tools or access to a financial advisor.

Then they have day-to-day finances — spending, earning, and investment patterns — that help to meet bigger goals. A personal finance management app can help them get better with regular spending and meet both short-term and long-term financial goals.

Most people end up using several solutions that each fulfill a specific financial need at a macro or micro level.

And when there’s a gap in the market, there’s a huge opportunity for success. Let me show you how you can fill in this gap by building a new-gen personal finance management app that will help your customers both intelligently manage everyday spending and plan for the long term in four steps.

Let’s dive in!

Four steps to building a new-gen personal finance management app

1. Data collection

Data is key to building rapport with customers as it helps to learn about their spending habits and teach them better ways to spend.

The Anatomy of a New-Gen Personal Finance Management Application 2
Anna Oleksiuk

A basic personal finance management app should offer:

  • Transaction categorization
  • Basic spending analytics
  • Spending visualizations
  • Customer data enrichment

Customer data enrichment is the central point here, as most users want more clarity in exchange for the information they provide. To deliver on that expectation, it’s worth aggregating the following data:

  • Transactions from connected checking and savings accounts
  • Traditional and alternative credit card data
  • Balance data from brokerage and investment accounts
  • Ratings from credit scoring companies
  • Balance data from retirement investing (IRA) accounts

The next logical question is how to collect, store, and process all that sensitive information securely and in compliance with GDPR, PSD2, and local regulations.

Complying with these regulations may appear to be a major roadblock. That is, unless a business places a solid data governance framework at the center of their operations. The goal of a data governance framework is to help understand everything about data: where and how it’s stored, where it comes from, what its value is, and how different people and applications use it.

Here’s an example of how a basic data governance framework looks like.

The Anatomy of a New-Gen Personal Finance Management Application 3

Source: TU BerlinImplementing Data Governance within a Financial Institution

Working on the data governance framework, me and my data science team at Intellias have prepared several actionable tips that would help to formalize a data management strategy:

  • Ask the right questions. Which customer problems to solve first? How much value can the solution generate? Answers to these high-value questions will help to determine which data sources to leverage.
  • Pursue feasible goals. Benchmark objectives against technical feasibility. At the onset of your advanced analytics program, it’s worth pursuing use cases that are feasible and bring ROI in the short term. As well, it’s necessary to make sure there is sufficient historical data available to support certain use cases and make sure that those use cases would be valuable to a critical mass of clients.
  • Perform a data gap assessment to understand the shortcomings in data/supporting architecture.
  • Map out the scope of architectural changes required to integrate new data sources and enable new analytics capabilities.
  • Create a formalized roadmap that outlines all the new components and architectural changes required and indicates the next build steps.

2. Insights discovery

According to Neal Lathia, Machine Learning Lead at Monzo, it’s necessary to shift the mindset from writing queries to answer received questions to writing queries to create data models that would address various types of questions.

A steady flow of data not only helps to ask better questions, it also helps to deliver better answers to customers.

With the customer data analytics, businesses have a better understanding of a user’s current standing and goals as well as pitch the next steps for getting closer to these goals. Fine-tuned app functionality for money management can translate gathered analytics insights into the following features.

  • More advanced spending analytics – estimates of monthly spending, untapped savings opportunities, upcoming bills, and direct debit payments
  • Bill consolidation and subscription optimization – notifications about upcoming bills to make sure users have the money to cover them; services to locate and cancel unwanted subscriptions and suggest strategies for lowering bills (like Truebill does)
  • Overdraft prevention – alerts about low account balances in light of large upcoming bills or regular purchases
  • Credit card debt management – reminders of when it’s time to pay off credit cards to minimize interest rates and other fees
  • Personalized offers and discounts – suggestions for products that closely align with a customer’s financial goals and life stage
  • Investment opportunities – suggestions for the optimal portfolio allocation for individual customers and help with choosing the right savings and investment plans

3. Financial coaching

Traditional analytics can assist with basic decision-making. Helping your customers with more complex planning will require bigger guns: machine learning, predictive analytics, and artificial intelligence (AI).

Machine learning algorithms can use all available internal and external data about individuals and advise them just like a wealth advisor would.

For instance, you can create an algorithm to analyze a user’s monthly cash flow, propose a monthly budget, and suggest savings opportunities. An algorithm can also provide real-time advice on how much users need to save daily to fund their dream vacation or make a down payment on a condo.

Credit Karma captures over 2,600 different data attributes per user, and their algorithm makes prediction among 8 billion options about what the right product is for a given customer at their current stage of life.

As well, you can develop a next best action model that discerns which product is worth pitching to a customer right now based on their financial and life circumstances. Early pilots have shown that such algorithms can result in a 30% increase in sales. Technologies can even help businesses build an AI-powered financial assistant for their customers to improve money management.

Predictive analytics is another key component of new-gen personal finance coaching apps. Again, using data, you can help your customers build long-term plans by providing them with an outlook on their finances.

Some of the popular use cases of predictive analytics in personal finance management are:

  • Predictive monthly spending and OK-to-spend budgets
  • Estimated and future net worth
  • Debt management and repayment plans to become debt-free by date X
  • Retirement planning
  • College fund planning
  • Loan refinancing and predictive debt management

The best part? You no longer need to develop a predictive analytics solution from scratch but can choose to go with an integration instead. Here are several solid FinTech partners for banks:

  • Salt Edge – a customer data enrichment platform that enables advanced financial analytics
  • Meniga – a white-label personalized digital banking software provider
  • Strands – an AI-driven PFM product

The application of machine learning in FinTech and banking is going far beyond customer analytics these days, including loan approvals with lower risks, security reinforcement, and enrichment of customer experience.

4. Autonomous finance – where the industry is heading

Today, software helps to extract the key points out of data we have and make right decisions.

Despite the abundance of personal finance management tools, nearly half (46%) of consumers still find finances overwhelming.

Artificial intelligence is coming to change that. Several market players are already working on a set of fully autonomous financial products — apps that provide accurate, personalized, real-time financial advice.

And most customers are on board with setting their finances on cruise control:

How comfortable would you feel using each of the following services?

The Anatomy of a New-Gen Personal Finance Management Application 4

Source: QuartzGet ready for self-driving mone

So what will autonomous banking look like?

Andy Rachleff, CEO of Wealthfront, states that their vision is to deliver fully automated services with no need for customers to interact — pay bills, top off emergency fund, and then route money to whatever account is the most ideal for customers’ particular goals.

Autonomous personal finance management solutions will be capable of coordinating activities to help customers reach multiple financial goals simultaneously — all with little to no daily input from the customer.

And such a scenario is less futuristic than one might think. Today, we are already seeing the rise of autonomous single point solutions:

Established market players with mature data infrastructures and a wealth of accumulated customer data are poised to become one-stop shops for automated money management.

Let’s compare the four market leaders:

The Anatomy of a New-Gen Personal Finance Management Application 5

Each of the companies listed above has one core automated FinTech offering: investing, credit score management, or loan refinancing. Today, these companies are actively extending the autonomous component to interconnect and power all products in their portfolios.

All four are also moving into banking, offering savings/checking accounts to their customers. This, in turn, will give them even more insights into their users’ day-to-day financials and help them extend their suite of autonomous offerings to daily personal financial management.

Conclusions

First, we had spreadsheets. Then came basic personal finance management apps. Now we’re moving towards an era of personal financial coaching, spanning either one area (budgeting) or several (retirement, investing, personal savings). We’re gradually entering the era of single-pane dashboards that will allow us to see all our key financial data in one place and dispatch AI to manage our finances for us.