COVID-19 has accelerated the process of digital transformation of the financial sector; Containment and social distancing have resulted in increased online shopping, greater use of e-banking, m-banking, or third-party payments, and as a result, higher than expected growth in transactions and payments. digital. The National Commission for Markets and Competition has encrypted the money that moved e-commerce to Spain in 2020, to over 51,600 million euros, just 14,600 million euros in the last quarter.
The response capacity of the financial sector has been brilliant during the pandemic through the incorporation and use of innovative technologies, such as Big Data or artificial intelligence, capable of standardizing, analyzing and processing. huge volumes of data to assess the real situation, detect patterns of behavior and develop effective future strategies. If there is one sector that relies on data management and analysis, it is the financial sector, where Big Data is already essential. Algorithms help detect fraud and measure credit risk, in addition to working with entities to understand their customers to resolve issues and improve the customer experience. Its use contributes to the reliable and secure development of digital and mobile banking with more secure and more numerous online transactions.
The technology, says Antonio Garca Rouco, CEO of GDS Modellica, “will not only change customer behavior, but will enable new risk management techniques with advanced analysis. The proliferation of new technologies offers a high level of execution and data storage with lower and faster costs This allows better support for risk decisions and process integration. According to GDS Modellica, among the benefits that Big Data brings to the financial sector are: processing automated and optimization of data analysis; more accurate risk assessment and prevention, identifies patterns and trends, competitor analysis, detects business opportunities, simplifies procedures, greater operability, improves business ‘customer experience, facilitates decision-making, segmentation of customers by finished profiles to offer personalized products adapted to your needs. Big Data is used in the financial sector to:
Proactively preventing fraud and protecting, analyzing large amounts of data and transactions in real time detects already known frauds, identifies risks or anomalies in user behavior to take imminent action. Automatically assessing the creditworthiness of clients, the algorithms allow an analysis of the applicant’s economic situation to find out who their clients are, determine risk and determine whether they can afford the loan. Minimizing the risks of financial decisions, being able to analyze and process data, in real time, makes it possible to know the real market context, the situation itself, what the risks are and to act accordingly. The improvement of security mechanisms and policies in transactions, the fight against fraud in real time, optimizes processes and helps prevent possible fraud. An analysis that ipso facto activates security mechanisms and policies in transactions. Adapting to financial regulations to prevent fraud, complying with PSD2 strengthens the security of electronic payments and avoids possible fraud scenarios. 6.- Cost reduction, by increasing operational efficiency, fraud is reduced and, therefore, efficiency and customer satisfaction are increased. Improving the customer / user relationship and experience, the machine learning capability promotes more accurate prediction of risk and fraud, identification and prioritization of possible fraudulent transactions. This translates into greater loyalty and increases customer retention capacity. Personalizing experiences, analyzing customer data provides information that helps to better understand customers, segment them by profile and offer products tailored to their needs.
The insights gained from the data analysis, according to Garca Rouco, allows banks to leverage customer payments, spending behavior, social media presence and even online browsing activity to make risk decisions. . The management of sensitive data involves difficulties, hence the need to resort to specialized companies, such as GDS Modellica, whose solutions and technologies are designed to manage risks and fight against fraud in order to meet customer needs and optimize their resources and map strategies. and new opportunities.