BPM Analytics

Unveiling investment futures: The power of predictive modelling

Banks, financial institutions, and investment firms are leveraging predictive analytics to forecast problems and risks proactively. CFOs are maximising the benefits of predictive analytics for customer acquisition and retention, risk management, stock-market forecasting, and more. According to a study, the global market for predictive analytics will grow to $41.52 billion by 2028.

This article explains financial services analytics using predictive modelling, analytics models, benefits, and use cases.

Predictive analytics in finance

Predictive analytics ingests and analyses large chunks of data in the finance and investment industries to predict future outcomes. It uses machine learning (ML), data mining, artificial intelligence (AI), etc.

Furthermore, it uses statistical methods such as pattern analysis and regression techniques to help you make data-driven decisions, predict and mitigate risks, and improve efficiency. Some of the sources of data for predictive analytics in finance are –

Historical market data

This includes manual and automatic data from past events in the financial markets and within the organisation. It covers but is not limited to press releases, financial statements, log files, email correspondence, product and project documents, and communication history.

Historical data highlights an organisation’s performance, areas that need enhancement, and future trends.




Economic indicators

This includes data on a macroeconomic scale that helps predict the economic well-being of the company. It also covers indicators on the national scale that come from government and non-profit organisations. Examples include gross domestic product (GDP), consumer price index (CPI), and unemployment statistics.

Economic indicators make more sense when you analyse them over a long period. Several indicators work in cohesion, which may not offer substantial insights standalone.




Sentiment analysis

This includes reading and interpreting the social sentiment around a brand, service, or product. It puts special emphasis on monitoring the online dialogue on social media channels, third-party platforms, and online reviews. Deep learning algorithms scrutinise text and deploy artificial intelligence to uncover valuable insights.

Predictive analytics models in finance

Predictive analytics models are standard techniques that utilise data sources mentioned above to forecast the future of finance and investment. Three predictive analytics models are:

Classification models

It is a subset of supervised machine learning and categorises customers or prospects into segments or groups. It does so based on historical data and defines relationships among the segments within the dataset. It is highly useful in fraud detection, risk analysis and management, and credit risk evaluation. It includes the following methods –

Regression analysis

Identifies the relationship between entities and defines patterns in massive datasets. For example, it helps to know how the price variance affects sales.

Decision trees

Categorise data into different variables based on other parameters. It is useful when several variables are missing from a dataset and helps predict a customer’s end decision.

Neural networks

This technique models complex non-linear relationships in datasets. Neural networks work when there is no mathematical formula to analyse and can validate the results of decision trees and regression analysis.

Clustering models

It groups entities by identifying differences and similarities between them. For example, a banking business can use this to segment customers into groups based on similar features and personalise the product and service strategies accordingly.

Time series models

These models use data inputs at a particular frequency, such as daily, weekly, or monthly. The model plots the variable against time to understand the patterns, trends, and cyclical behaviour.

Why use predictive analytics in finance?

Customer data analytics is critical for the finance and investment sector, and lately, businesses have been turning to predictive analytics for these benefits:

Organisational agility

Predictive data analytics tell you what your customers expect so that you can proactively make the necessary changes in your functions. This forward-thinking approach gives greater organisational agility.

Better sales strategy and tactics

You can design sales strategies knowing what will work and what will not. This lowers the customer acquisition cost and allows you to upsell or cross-sell.

Improved decision-making

Investment managers can use predictive analytics to understand better where to invest their customers’ money. They can easily identify potential opportunities and risks of different investments.

Risk mitigation and management

Identify and manage risks associated with different investment strategies. Simulate scenarios and analyse the data, compare it with historical data, and do quantitative analysis to adjust strategies.

Predictive analytics use cases in finance

Planning investments and identifying opportunities are not the only use cases of predictive analytics in finance. Several other significant use cases are –

Cash flow forecasting

Use data analytics to identify slow players, improve receivables management, and solve fundamental issues in the cash flow to elevate the financial health of your business.

Fraud detection 

Contactless and fast transactions over digital media have taken over, giving rise to several frauds and scams. Data analytics with artificial intelligence and machine learning can detect an anomaly in real time to classify the risk.

Budgeting and resource allocation

Gather data patterns and determine whether allocating the budget within that discipline would yield results. Increase spending in areas that have historically performed well and cut costs in areas that have not.

How can Infosys BPM help you with predictive modelling?

The financial service analytics offer customised AI and ML capabilities with visualisation features to boost profit margins. Generate actionable insights from structured and unstructured data across product portfolios.

Read more about financial services analytics at Infosys BPM.