Top Five Ways AI Is Transforming The Financial Planning Industry

Financial planning and analysis has always been a high-reward, high-risk sector. As a result, disruptors are always searching for ways to mitigate risks and maximize opportunities. Against this background, AI has emerged as a blessing that allows individuals and organizations to benefit from the technology. It brings a handful of advantages ranging from greater compliance to real-time monitoring.

On that note, let navigate through the different segments of the financial planning and analysis (F&PA) sector that are experiencing a revolution propelled by AI:

Analyzing

The major offering of any Artificial Intelligence solution rests in its capability to methodically analyze data and generate relevant and valuable insights that are not visible to the human eye. Any type of analysis becomes all the more complex when it involves numerous dynamic and variable parameters. But, AI remains unperturbed by changes and manages to recognize patterns, analyze cash flows and dubious flag activities and fraud detection. As a result, organizations can utilize it for various applications, from budgeting to transaction validation to credit scoring.

An organization’s marketplace, for instance, offers a text analyzer tool that can process long and detailed news or financial articles and show a snippet containing the core extracts. Similarly, the asset summary generator can assist you analyze the reasons behind stocks’ movements. Another way AI helps analyze different asset classes is by offering sentiment analysis. Artificial Intelligence tools are renowned for their ability to offer accurate sentiment analysis. This is helpful for investors who’re momentum traders and are interested in investing in a stock that has an improving sentiment.

Forecasting

Forecasting has proven to be a highly sorted quality throughout the Financial analytics market, and AI makes it possible with its data-driven approach. Hence, it should be no surprise that predictive analysis is one of AI’s most well-known and well-loved limbs in F&PA.

It finds widespread applications across different verticals, from predicting customer behavior to forecasting project spending. These can even boost financial advisory tools that may calibrate spending habits based on lifestyle indicators. Likewise, it can act as the north star for intraday traders to time the market and make actions like increase position, wait, or withdraw depending on their risk appetite, closing and opening prices, the possibility of profit/loss, and more. For example, Artificial Intelligence is utilized to make revenue projections. This is especially helpful for investors who are investing based on earnings announcements. AI that can essentially accurately predict future revenue can be invaluable for investors.

Planning

While analysis is positioned to the present conditions, forecasting assists with future predictions. And planning plays the role of a bridge that merges the two states. Artificial Intelligence can assist individuals and companies draw a practical roadmap that connects their present state to their future goals. Like, it can power Robo-advisories that guide consumers on investments to reach their retirement or financial independence objectives. Similarly, Artificial Intelligence can help businesses factor in any difficulties that may impact the costing of any project and how to trigger the same. The actionable inputs provided by Artificial Intelligence ensure that the planning process follows the shortest course while realizing the end goals.

For instance, an exposure analysis tool can generate a list of assets susceptible to specific market exposures, which can help investors plan their portfolios. The tool can answer queries such as — how Brexit affects disparate asset classes. This can be useful in knowing how exposed your portfolio is to a specific event like Brexit.

Reporting

Even though data is useful in numerous ways, an excess of data can prove to be overwhelming. High-volume data can paralyze decision-making or delay, so companies miss out on capitalization opportunities.

Fortunately, new-age AI applications employ natural language generation (NLG) to assist businesses cut through the competition and get to the depth of any report. As an illustration of the same, AI based organizations offer an array of tools and features that can process reports to provide credible insights through asset summary generation, report translation, text summary creation, sentiment analysis, and more. Therefore, you can use it to collate and visualize information in the most digestible forms.

AI tools provide excellent summaries of various news and articles and allow investors to compare financial projections from different sources — for example, predictions made by multiple analysts regarding the companies’ revenue.

Automating

Artificial Intelligence is lending direction to robotic process automation (RPA) bots that can automatically perform rule-based and current tasks without manual intervention. According to Gartner, a single bot can displace as much as 30x times the work of a human, which can be a boon considering that F&PA can be highly labor- and resource-intensive.

To put this gain in perspective, Gartner monetarily quantifies it to highlight that Robotic Process Automation costs one-third of the cost spent for employing offshore staff and one-fifth for onshore employees. Most critically, RPA translates decisions into the realms of real-time action that will improve your bottom line.

Final Thoughts

The Financial analytics market is a hotspot for Artificial Intelligence-led tech innovation. Organizations can accordingly deploy Artificial Intelligence solutions to discover multiple drivers for their business, enhance real-time decision-making create accurate forecasts, and improve Return On Investments. With such loaded advantages, what more could one want?

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Polestar Solutions | Data analytics company

As an AI & Data Analytics powerhouse, PolestarSolutions helps its customers bring out the most sophisticated insights from their data in a value oriented manner