Level up the performance of financial services

Shift safely to a cashless society

The current outlook of financial services:

The pandemic accelerated what was needed to form a cashless society. Due to the radical digital revolution, financial services firms have begun to face a slew of challenges. They have become ideal targets for cybersecurity threats as contactless payments took the center stage. This is primarily attributed to the critical customer information they hold. Consumers continue to have high expectations from their financial institutions, as they want more personalized services with frictionless experiences. Institutions that provide all of these services will control a large portion of the market. To keep pace with rapidly changing consumer expectations, technology evolutions, and industry regulations, traditional financial services must constantly evaluate and improve their operations. Brand loyalty of companies will take lesser precedence as compared to companies offering superior and secured experience to stay in the race.

Business Challenge:

Prior to the pandemic, banks were location-centric, with employees tied to specific locations. Inefficient onboarding processes and long cross-border trading and settlement takes a lot of time. As a result of new found trust in digital solutions, banks are more likely to adopt new technology for maintaining and improving their performance. The traditional resistance to change in the more antiquated corners of financial services has dissipated. New technology adoption raised the challenge of handling various types of data and brought financial services among the most data-intensive industry. Conventionally, humans did the complex computations, and decisions were made based on conclusions drawn from calculated risks and forecasts. On the other hand, fraud and criminal acts are on the rise in financial services. It must safeguard their most important piece, which is the trust placed in them by their customers. It increases the importance of the security of interaction networks and customer data. Financial services must shift away from formulaic customer journeys to adaptive experiences. Banks should think about their subsequent data needs and start collecting them now to support new forms of customization.

Solution:

In volatile market conditions, banks’ ability to use the information at their disposal to select the most relevant investment options for their clients at the right time has become a key competitive differentiator and driver of profitability. Big data enables financial services to monitor, analyze, and improve decisions based on lessons learned and feedback from customers. The bank can process massive amounts of transactional data, including billions of records per day. Big data can process real-time logged data streams from all monitored banking systems. Customer banking transactions and related data are accessed by big data. Customers with loans are elsewhere identified. Big data provides valuable user behaviour insights, allowing banks to deeply understand their trends and prohibit suspicious activities. Even slightest of differences found in customers trend and pattern while doing financial transactions or credit card purchases can be automatically analyzed and flagged proactively. Fraud detection using data analytics has improved vastly due to better programming languages and server technology. Furthermore, Big data helps in identifying the problems in product targeting, and offers actionable insights to take relevant steps to enhance the overall customer experience. Banks are crunching massive amounts of data to identify potential rogue traders using big data.

Business Benefits:

• Risk prediction: Data analysis can identify situations where a financial crisis or security issue is likely to occur. Risk assessment can be performed for a better understanding of the customer. Big data analytics in financial services can improve cyber resilience and mitigate risks.
• Valid Authentication: Big data promises comprehensive lines of risk-based authentication, in which a fraud-detection engine calculates a risk profile for each channel request, determining the required level of authentication. One can detect fraud and prevent potentially malicious actions by using intelligent algorithms.
• Simplify core systems: Big data can handle everything from customer behavior patterns to internal process efficiency and even broader market trends. As a result, one can make informed, data-driven decisions and achieve significant performance improvements while lowering operating costs.
• Convert data into a sale: Big data analysis assists people in learning about details such as demographics, transaction details, personal behavior, and so on. Banks can create a separate list for such customers and target them based on their interests and behavior based on this data.
• Eliminate human errors: By eliminating manual interference from several critical operations, banks can reduce the possibility of failure.