How AI Can Help Produce Accurate Economic Forecasting
By: Robert Guthrie
Tags: Economics, AI, Risk Management, Machine Learning

The processes of economic forecasting were built on a flawed foundation — often, they fail to factor in critical disasters that could slump the economy overnight. Moreover, many forecasters are influenced by personal or organizational interests, resulting in biased analyses and inaccurate predictions. Take, for example, the financial sector: one of the unluckiest recipients of the inaccurate system. Forecasters could not predict 148 of the past 150 recessions.

While organizations have long relied on Business Intelligence (BI) technologies to collect and analyze data, this practice doesn’t make much headway in the field of economic forecasting. The data from BI tools is only good for measuring what’s already there. The future is an entirely different arena.

According to PWC, CEOs see huge potential in the use of Artificial Intelligence: 72% of them believe it will help people focus on more important tasks. AI and Machine Learrning (ML) could also be the solutions to inaccurate forecasting.

Here, we’ll discuss economic forecasting based on AI and MI helps your business.

How ML and AI Solve the Forecasting Problem

AI produces faster, more accurate results. It organizes and supplies data to support decision-making and productivity. AI systems that address forecasting include:

Expert Systems. These compile existing knowledge and laws. (knowledge is stored in a set of ‘if-then’ rules) This knowledge is collected by polling experts or combining data sets. For instance, it is used in predicting the weather using prevailing temperature, humidity, seasons, and locations. Belief Networks. Belief Networks define the database infrastructure through a tree form. The tree nodes depict variables while the branches represent limited dependencies within variables. Belief nets create conditional probabilities for several future results. For example, predicting sales based on marketing, customer dissatisfaction, and budgets. Neural Nets. Neural Nets mimic human brain functions, particularly recognition and learning of patterns. The infrastructure includes a network of nodes that calculate input and send results within the network. For example, neural nets can be used to forecast worker turnovers by categories such as length of employment and salary.

At Geospark Analytics, our AI platform Hyperion utilizes neural nets to find patterns in regional and national stability. It also is continuously updated according to new information, such as Tweets about breaking news events, thus learning and adapting in a way comparable to the human brain.

AI technologies promise to revolutionize forecasting. Businesses will be able to predict demand for goods and services, employee departures, cash flow, and labor needs. These systems will link the redundant managerial and quantitative methods of forecasting to produce a smarter system.

Opportunities to Leverage AI Forecasting

Opportunities to use AI forecasting are endless, given the current limitations. For example, multifaceted AI-enabled economic modeling empowers researchers to predict and examine the likely impacts of extensive variables on the economy. While traditional modeling has only two dimensions, AI encompasses many more. The advanced modeling improves policy analysis, academia, and other industries. The IRS and other government arms concerned with budgets, taxes, and other processes that demand economic modeling also stand to gain a lot from AI and ML efficiency.

Moreover, AI is extending the capacities of modeling to make research more effective. AI techniques used in behavioral science can leverage data to discover details that change statistics in small, previously hard-to-analyze populations. Multidimensional economic modeling can produce a high quantity of simulations in the same graphical scope in real-time to analyze the behavior of complicated economic aspects.

Artificial Intelligence can support businesses and industries through superior economic research. Some examples of AI-driven capacities include:

Monitoring market trends locally and internationally using current data on money, credit, and/or foreign exchange. AI algorithms can also pick up on emerging harmful economic issues.

Analyzing developing trends or issues in sensitive sectors to measure impact on the financial risks.

Collecting copious data for detailed economic studies, thus supporting policymaking by implementing procedures that are aligned with the law.

Helping decision-makers predict the impact of change in regulations.

Expected Benefits of ML and AI-Based Data Analyses

As explained in this blog, Artificial Intelligence offers an incredible array of economic benefits to businesses and industries. AI offers the most advanced system of fraud detection. Traditional algorithms detect fraudulent activity by a breach in preset regulations. ML algorithms are smarter at fraud detection. They use behavior studies to spot suspicious activity before any rule is broken.

For example, if an account that consistently holds a small amount of money suddenly receives a huge check, conventional algorithms only raise alarm when the amount exceeds a certain preset amount. ML algorithm on the other hand holds the check until a human being verifies the validity of the transaction. AI also learns from previous history to detect unusual activity.

Who We Are

At Geospark Analytics, we help businesses and organizations leverage the speed and accuracy of Artificial Intelligence to keep you ahead of the competition. Contact us today for ML and AI solutions, and we’ll be more than glad to help.