The 8 Benefits of Big Data for transactions in the financial sector
Big Data provides the financial sector with security and reliability. Online transactions are increasingly more numerous and secure thanks to the assessment mechanisms and real-time fraud detection provided by Big Data
GDS Modellica provides decision analysis and machine learning solutions to guarantee transaction security, manage risk and combat fraud
COVID-19 has accelerated the digital transformation of the financial sector. Lockdowns and social distancing have led to an increase in e-commerce purchases, greater use of electronic banking, mobile banking and third-party payments and a greater than expected increase in digital transactions and payments. In Spain, for example, the National Commission of Markets and Competition has estimated that e-commerce moved more than €51.6 billion in 2020, with €14.6 billion just in the last quarter.
The financial sector’s ability to respond to the pandemic has been brilliant thanks to the incorporation and use of innovative technologies such as Big Data or Artificial Intelligence, which are capable of standardising, analysing and processing enormous quantities of data to assess the present situation, identify behaviour patterns and devise effective strategies. If there is one sector that is built on the management and analysis of data, it is the financial sector, where Big Data is already an absolute must-have. Using Big Data, algorithms can help detect fraud and measure credit risk. These same algorithms can also be used in collaboration with other organisations to better understand their customers, resolve problems and improve the overall customer experience. All of this helps to ensure the steady development of digital and mobile banking with more secure and numerous online transactions.
In the opinion of Antonio García Rouco, managing director at GDS Modellica, technology “will not just change customer behaviour but also enable new risk management techniques with advanced analysis. The emergence of new technologies provides faster processing and greater data storage capacity at lower cost. This provides better support for decision-making, risk management and process integration”. According to GDS Modellica, the benefits of Big Data for the financial sector include: optimised and automated data processing and analysis; more precise risk assessment and prevention; simplified procedures; competition analysis; the ability to identify trends and patterns; better identification of new business opportunities; an improved customer experience; better decision-making; and the ability to segment customers by profile in order to offer personalised products suited to their needs. Currently, Big Data is used in the financial sector to:
1.- Proactively anticipate and mitigate fraud. Being able to analyse large quantities of data and transactions in real time helps to detect known fraud and identify risks of anomalies in user behaviour to take immediate countermeasures.
2.- Automatically assess customer solvency. Algorithms make it possible to analyse an applicant’s current financial circumstances and learn about their customers, their level of risk and whether they are likely to meet their obligations.
3.- Minimise the risk of financial decisions. By analysing and processing data in real time, it is possible to assess the real market situation, what the risks are and act accordingly.
4.- Improve security mechanisms and policies for transactions. Big Data can help combat fraud in real time, optimise processes and prevent possible future frauds through analysis that can be fed into security mechanisms and policies.
5.- Adapt to financial regulations to prevent fraud. Complying with PSD2 reinforces electronic payment security and helps prevent potential fraud.
6.-Cost reduction. By increasing operational efficiency, fraud is reduced and customer satisfaction is increased.
7.– Improve the customer relationship and user experience. Machine learning capabilities make it possible to more precisely foresee risks and frauds, identifying and prioritising potential fraudulent activities. This leads to greater customer loyalty and retention.
8.- Personalise the customer experience. Analysing customer data produces information that helps to know customers better, segment them by profile and offer them products suited to their needs.
According to García Rouco, the information obtained from data analysis makes it possible for banks to make better decisions based on customer payments, spending habits, social media presence and even online browsing activity. But managing such sensitive data has inherent difficulties and that is why there is a need for specialist companies like GDS Modellica, whose technology and solutions are designed to manage risk, combat fraud and increase customer satisfaction by optimising resources, devising successful strategies and identifying new opportunities.