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AI Financial Planning Program

What Is an AI Financial Planning Program?

More than a static budget sheet in Excel, an AI financial planning program helps users set budgets and analyze their spending behavior. With a good ML program, company and personal budgets perform better—faster than via traditional methods.


Why Is It Important to Have a Good AI Financial Planning Program?

The AI component of a good AI financial planning model updates and analyzes spending behavior automatically. This allows you to reduce excess spending before it becomes a problem or simply update your budget. In fact, more than just showing you what you have done, machine learning can offer suggestions for how to better meet your budget goals.

AI can also analyze fraud risk in your suppliers or partners, ensuring your finances stay secure.

What Internal Data Should I Have for a Good Financial Planning Program?

Most AI financial planning data is internal, beginning with your goals. You will also need data about your account details, credit cards, employees and other fixed expenses, variable expenses, contractors, etc.

What External Data Is Essential for a Good Program?

Of course, you must record in full all transaction data into your budget planning program once active. Further, if you need to monitor the budget of a client, you will need all their transaction history and goals. Beyond this, however, there is not much external data that is truly essential—just very useful.

What External Data May Prove Useful for a Good Program?

AI-based budget programs benefit immensely from fraud detection, compliance, and NLP machine learning programs. Fraud detection and tax or industry compliance procedures have obvious uses but Natural Language Processing programs less so. Nevertheless, NLP programs are extremely useful as they are built to understand user-submitted transaction notes and communicate with users about how to plan and stick to budgets once implemented.

While these are not data sources themselves, they do need to be updated with the latest industry data. You don’t want to miss any new tax reforms or breakthroughs in fraud detection programs.

You may also consider enriching your AI budgeting program with price comparison data, to cut down on time spent searching for the best deal on whatever you need.

What Are the Main Challenges of this Use Case?

There are three main challenges with building an AI financial planning program: data input, standardization, and security.

First, you need full and consistent transaction inputs from your team. While good AI programs can prompt users to fill in the details, this step depends on human users who may not record information correctly or in the same way, leading to some issues with standardization—the second challenge.

Related to the issue of cleansing, data standardization ensures that there are no duplicate or similar data categories that make the budget harder to understand and analyze.

Third, security: any financial program must be secure but the difficulties in a large budgeting program with multiple users accessing and updating the data from numerous insecure locations becomes a much more difficult problem. Multi-Factor Authentication (MFA) security systems help here, as does constant monitoring of the program for weaknesses in security.

Interesting Case Studies and Blogs to Look Into

Intellias: How to Build an AI-Powered Financial Assistant Application
Let’s Nurture: Implementation of AI in Financial Planning

Tangible Examples of Impact

Focus on data beyond simple demographics, KYC and RTQ. Expanding the data collected on prospects and clients is critical to enabling the delivery of highly relevant solutions, customised products and experiences, as well as necessary in delivering reciprocal value. It’s easy to ask for more data, but if clients do not derive reciprocal value, it won’t be provided. The data collected should go beyond ‘structured’ information – data about the client’s current accounts, savings, pensions and taxable accounts – into ‘unstructured’ data. This is information resides in emails, notes from client conversations and qualitative insights about their goals. It’s information about family, life concerns, lifestyle, needs and wants. Too often, this information is collected during an initial client meeting but then tossed aside in lieu of data points necessary to open an account, move funds, process a transaction or meet regulatory requirements.

Nasdaq: Changing the Face of Financial Advice with Data and AI

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