The Future of Financial Modelling

Financial modeling is an intensive technology process. This means that at the present moment also, financial models use the latest technology in order to ensure that the most up to date results are available during the simulation. However, with the passage of time, technology is also advancing rapidly.

Improvements like machine learning, robotic process automation, and artificial intelligence are bound to have an impact on the future of financial modeling as well.

In this article, we will have a closer look at some of the ways in which technology is likely to impact the financial modeling process.

Data Will be Collected Using Automation

At the present moment, a lot of point of sale data needs to be collected manually. This makes the process expensive, cumbersome, and prone to errors. Manual collection of data is necessary since sensors and processors have not become so ubiquitous. However, with the passage of time, the internet of things is enabling sensors and processors to be inserted in almost every device.

Hence, companies are producing large quantities of data which describe their processes without even trying! At the present moment, machines equipped with sensors are expensive. However, in due course of time, this technology will become cheaper. This will have huge implications for financial modeling.

For instance, right now, companies make vague estimates about their electricity bill. Devices fitted with sensors will allow companies to know exactly how much electricity is being consumed by which device. Therefore, companies will be able to forecast their electricity expenses more accurately and also control them, if required.

It is very likely that in the future, devices will be pre-programmed to input data directly into financial models. This will help bypass the expensive and time-consuming process of data collection and collation.

Big Data Will be Involved in Financial Modelling

At the present moment, financial models deal with a limited amount of data, and hence the complexity is also limited. This is likely to change in the future. As mentioned above, in the future, many devices will directly interface with the financial model. Therefore, financial models will start facing a problem of an abundance of data.

The next challenge would be to identify meaningful patterns in the data, which will enable decision making. This is where big data can help. At the present moment, big data is only used to mine customer or supplier data. However, as financial data grows in volume, this technology will find a new application in financial modeling.

Models Will be Constructed Using Automation

At the present moment, financial modeling is limited by the skill of the person creating the model. There are many problems in financial modeling, which include multiple variables and complex interactions between them. As a result, the human mind is not capable of understanding these relationships and expressing them in mathematical form.

However, computers are capable of performing such complex calculations. Hence, ideally, computers should be able to create better models given that they have the ability to do millions of calculations in no time.

Right now also there are many ready-made templates which are used by companies to create financial models quickly. However, these templates are primitive and still require a lot of interference from humans during the customizing process.

The future is likely to have fully automated financial models which can be used out of the box without any need for any further manual intervention.

Models Will be Updated Using Automation

Right now, human beings interpret the data from the models. They are the ones who understand the failure or success of the model and then make the relevant changes.

In the future, financial models are likely to have artificial intelligence. This means that the computers will be able to understand the success or failure of their own model. This will be done when the system compares the actual results to the ones projected by the model.

At the present moment, the problem is that computers do not have the full range of data. To make correct calculations, computers must have a wide range of data which encompasses possible successes as well as extreme failures.

However, the statistical methods right now are not so advanced that such data can be provided to the models. It is likely that this problem will be solved in the future.

Computer-generated models will be pre-programmed with every possible output which will help the model correct itself without any manual interference. Just like self-driving cars, financial models of the future will be able to fully function without any intervention by a qualified human.

The fact of the matter is that an enterprise is a complex organism. There are so many interactions within the enterprise at so many levels that it cannot be understood completely without the help of a detailed model.

Advanced technologies in the future will help us understand this complex environment and make difficult decisions. The computational base of modeling has already been established.

It is now time to create an ecosystem wherein data gets automatically fed into the system, and the system learns from its mistakes. Building artificial intelligence into financial models is the real challenge facing the financial modelers of today.