In the world of finance, making well-informed predictions on a regular basis is vital. Some examples include traders forecasting which directions in which stock markets are heading and lenders discerning a borrower’s credit risk. Fortunately, the rise of “big data” has eased such forecasting.
However, it isn’t just a matter of what data an organisation has at its disposal; there is also the question of what it should do with that data. This is where predictive modelling can come into play.
Plenty of data, but how to handle it?
The influence of predictive maintenance is being felt in various sectors. Manufacturers, for example, can use it to more reliably predict when equipment should fail and, therefore, replace it before then. Similarly, retailers can closely monitor their inventories without needing warehouse stock-checks.
In a world more connected than ever before, financial services can mine massive amounts of data before putting it to the purpose of informing continuous predictions. As it is aggregated, data for commercial use can reach terabytes, petabytes and exabytes, says ITProPortal.
With so much data becoming available, businesses could struggle to quickly and intelligently analyse it of their own accord. In fact, fulfilling this responsibility would be unfathomable bar the use of computing power and algorithms. Financial firms are certainly not unaffected by this situation.
Examples of what predictive analytics can and can’t do
In finance, predictive analytics can be applied in a broad range of useful ways. Nonetheless, it is crucial to precisely define what predictive analytics can do for companies in this sector – because, as acknowledged by Information Age, what it can’t do is tell what exactly is going to happen.
Instead, predictive analytics – a combination of artificial intelligence, machine learning and data mining – can assist financial services companies in visualising likely outcomes. For example, if such an organisation detects that 60% of a particular area’s residents would likely consider a specific service, the company can feed this into its revenue projections.
Where the finance department ought to draw the line is at using those predictions to conclude how much revenue the company is guaranteed to make in the quarter. In itself, predictive analytics is unlikely to produce problems – but overly relying on the results’ validity could result in such.
From Excel to excellent: how a cloud transition can work well
Modelling is another part of predictive analytics, but forming accurate and scalable predictive models can be difficult for a company that continues to use traditional Excel data sets. Though predictive modelling has its roots in the Microsoft spreadsheet program, the software has particular limitations for companies seeking to further their financial predictive analytics as the firm grows.
These companies would be hindered by limits in memory, processing power, security and control. It would also be beyond practical possibility for them to scale on-demand, while cash-strapped firms, in particular, could struggle to continue funding on-premises infrastructure on which they rely.
Examples of predictive models and their possible purposes
Still, financial firms can throw all of the above-cited fetters off their predictive analytics endeavours when their Excel data is transferred to the cloud. RedPixie, a consultancy which provides cloud solutions for financial firms, can help your financial organisation to move its Excel data to Azure.
This would give your organisation sufficient room in which to continue building usefully sophisticated predictive models. There are various ways in which such models can work in practice.
Your company could choose a predictive model to discern brokers and merchants that are particularly excelling and, thus, with which you should pursue making deals. Through narrowing down the opportunities in this way, you could ultimately assist in boosting your firm’s profitability.
A separate model could measure the market impact of trading decisions with which your business proceeds. The model could reach such a high frequency that you pick up on trends and price fluctuations.
We recommend Azure due to its many capabilities as a cloud computing platform. One good case in point is how this Microsoft solution can be used in determining credit risk. The Redmond company has demonstrated how a particular Azure model can detect such risk by, when analysing text, looking for medical problems, credit card fraud indicators or rudimentary errors.
On RedPixie’s website, you can find out more on how predictive modelling is shaping the financial sectors. The additional information on the company’s website includes how a model can be put to the purpose of determining insurance risk. Of course, you can also learn how RedPixie could help you.