Financial market players have always been looking for new ways to reduce costs, improve controls and uncover fresh insights that can drive competitive advantage. Today, with the fast growth of data-driven technologies, they turn their attention to machine learning and artificial intelligence. According to a Gartner survey, 27% of financial departments expect to deploy some form of artificial intelligence or machine learning and half of the respondents — predictive analytics by 2020.
Although many organizations aspire to use AI to improve financial planning and analysis (FP&A), only a few succeed in it, since the technology is not yet built into most FP&A application suites and consequently not well understood.
Let’s think of cases where AI can significantly help financial departments:
It is well-known that FP&A embraces a comprehensive quantitative and qualitative analysis of all operational aspects of a company to evaluate its progress and to outline future plans. FP&A Analysts consider such parameters as economic and business trends, past company performance, and potential obstacles.
These components are closely interconnected and usually, AI-driven solutions address not one sphere, but combine analysis and prediction, uniting several tasks in one.
Analysis
The core of any AI-driven solution is the scrupulous analysis that can reveal insights otherwise concealed from humans. With many parameters that need to be taken into account, human experts can miss a part of the picture or miscalculate the importance of certain factors, whereas AI is known for its ability to work with multiple factors and assign them different weights to achieve sometimes unexpected results.
AI solutions we typically do for financial organizations concern patterns detection, money flow/transaction analysis, and detecting signs of fraud or suspicious actions.
Life example. Smart security transactions validation
One of the many routine tasks that are performed by support teams is transaction validation that is based on specific rules for specific cases. A possible solution is to apply machine learning algorithms that could map the rules to encountered cases and, in this way, could check and verify the majority of transactions. A small portion of transactions is validated manually to form a reference set. Such analysis speeds up transaction validation and reduces service costs.
Life example. Credit scoring service powered by AI
Credits are dubious. Financial institutions need to establish their clients’ credibility sometimes with little or no credit history. Analysis of unstructured data can reveal patterns of payment behavior to show the candidate’s willingness and ability to pay. Such models can measure the customer’s loyalty, purchase frequency and — with enough data — create common customer models and enable predictive analysis.
Forecasting
Predictive analysis is probably the most well-known and commonly used field of machine learning in financial departments. It can be applied virtually to all spheres, from forecasting future spending and revenue to predicting human behavior. In our experience, we created algorithms to detect trends, financial indicators and to predict people’s spending habits and lifestyles to take appropriate actions.
Life example: Trader’s patterns and predicting of trader’s next move
Humans do all kinds of work according to a certain pattern that is especially evident in routine tasks. Similarly, all traders have a certain pattern underlying their behavior, showing their attitudes to risk and reward. With the help of AI, we can create a trader’s profile and recommend the next step — to increase the position, wait or cut it down — according to the price movement. Such an AI solution studies the trader’s past trades and creates a trading pattern to predict the trader’s next move mimicking the trader’s behavior. Moreover, the model can predict the opening and closing prices for the trader, and the amount of profit or loss in a given market condition for a trader with a certain behavior pattern.
Reporting
One of the most recent developments in AI-driven reports is the use of natural language generation (NLG) tools to automatically populate different report forms and even generate financial reports and analysis of business intelligence data.
Life Example. Risk Reduction Platform for Banking
An example of how AI and Data Science unite all components of FP&A in one solution is developing a web platform to reduce bank risks. The bank uploads its clients’ transactions into a web application to detect suspicious transactions and possible malefactors. Data Science algorithms analyze a large number of transactions to detect anomalies with the help of heuristics, graph algorithms, and transaction flow analysis. Afterward, a common customer model is built to predict further customers’ behavior. In case of suspicious behavior or anomaly, the system generates a report about potential risks.
To generalize, financial organizations, in order to succeed with introducing AI in their daily practices, need to think globally of opportunities. As Gartner suggests, they should:
- examine current FP&A processes and tools;
- expand existing financial analytics capabilities; and
- pursue all FP&A AI opportunities.
There are many out-of-the-box ways for AI to support partnerships between finance and LOBs, by providing analytics and decision support and through integrated financial planning and modeling. Use them well, and you’ll get to know your figures — and your clients — beyond the usual human scope.
