bankingciooutlook

Data Analytics and Its Implementation in the Banking Sector

Banking CIO Outlook | Friday, July 17, 2020

Customer expectations have changed drastically over the years. They are unhappy about the fees paid and demand convenience and accessibility, such as mobile applications.

Fremont, CA: The banking and finance sector has experienced some tectonic shifts in recent years with the advancements in technology. Changing regulatory policies and increasing customer expectations have forced banks and other financial institutions to adopt the latest trends and deliver improved services in a highly competitive market. If leveraged appropriately, disruptive technologies can provide significant cost savings, improve customer service, and help gain or retain a competitive advantage. With every new opportunity arises new challenges that need to be addressed. Customer expectations have changed drastically over the years. They are unhappy about the fees paid and demand convenience and accessibility, such as mobile applications. The increasing competition in the sector means banks cannot afford the luxury of overlooking these demands. Top 10 Customer Experience Solution Companies - 2020

The rise of fintech companies has made the market more competitive than ever before. Fintech companies have played a significant role in the acceleration of the adoption of changes. Banks are now under tremendous pressure to lower their rates and improve overall efficiency and effectiveness. As a response, traditional banks have been working aggressively to leverage the latest trends in technology. This has skyrocketed the rates at which banks are adopting advanced data science tools.

Artificial Intelligence and Machine Learning

Artificial Intelligence (AI) and Machine Learning (ML) have become the buzzword in every industry. AI can be defined as the ability of a machine to perform cognitive tasks such as problem-solving, learning, perceiving, and reasoning. This has enabled faster and more consistent decision making. AI and ML use self-adaptive algorithms to identify patterns in data, which then can be used to make predictions about the probability of specific actions, such as customer default or early repayment.

Data Warehouses

The majority of disruptive technologies today are data-driven. This means a large amount of data is generated, which needs to be stored for analysis and interpretation. While a centralized data warehouse is an outdated concept for most organizations, it is essential in the banking sector. The shift to an external data lake only increases the burden on governance. The lack of a centralized data source would mean everyone would have to maintain individual data records. This created data swaps, which included numerous islands of uncontrolled data.

The adoption of Cloud

Cloud technology has been breakthrough advancement for all industries. Cloud technology eliminates age-old legacy systems and replaces them with a more modern system that enables real-time processes. More and more organizations are using cloud servers rather than local hardware to host data storage and business processes.

See Also: Top Banking Tech Solution Companies

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