The finance industry generates lots of data, which they can then use to produce meaningful insights to help with their operations. What’s more, it has changed the way financial organizations perform their daily activities.

Below, we look at some of the ways that the finance industry uses big data. 

Risk Analytics 

Risk Analytics is one of the key areas of data science in finance, and the main steps towards managing risks are identifying, monitoring and prioritizing risks. Organizations can use predictive analytics to take into account many factors (such as the economy and customer segmentation) to identify potential risks. What’s more, machine learning algorithms can be used to analyse customer transactions. This helps businesses make strategic decisions, and increase their trustworthiness of the business. 

Real Time Analytics 

Advancements of technology allow access to multiple data sources with minimal latency, allowing businesses to track transactions and credit scores in real-time. These data sources can be from both internal and external origins, and can be used to create data dashboards and drive business insights. 

Increased Personalisation

As online banking becomes more popular, and customers interact less frequently with face to face representatives, personalisation is a big must for the financial sector. In-depth knowledge about the customer is required, which can only be achieved by leveraging available customer data through big data techniques. Things such as browsing history and time of interactions/transactions can be collected, which can then be leveraged to provide personalisation – helping to compensate for the reduced customer interaction and personal interaction with the branch. 

Operational Cost Management 

Due to lower interest rates of the current economic climate, banks and insurers have to reduce operational costs to improve business efficiency. Many of these gains will be driven by the insights gained from big data.

Fraud Detection 

Fraud is a major concern for the financial industry, and the risk has increased with the increase in number of transactions made by individuals daily. However, big data and analytic tools have allowed organizations to keep track of fraud. Algorithms can increase the accuracy of anomaly detection, and alert organizations about these anomalies, allowing them to block accounts to minimise losses. Machine learning tools can also identify unusual patterns in data. 

 

To conclude, big data and analytics are playing an important role in how the financial industry adapts to new threats, as well as new data and information. If you are interested in more big data solutions within the finance industry, contact us now to see how we can help.