Unlocking new levels of operational efficiency in financial services

The financial services industry is changing. While the fundamental principles that the industry is built on remain the same—such as trust, value and customer service—the way financial organizations deliver on these values is far different from what it once was. We are now in an always-on, ever-connected world where banking customers expect to have access to accounts, information and services whenever and wherever they want, and the way organizations handle these operations can make or break the overall customer experience -  and the bottom line.

Financial services institutions need to find a balance between driving new innovations and keeping costs in check—all while meeting regulatory requirements. This culture of real-time engagement and access to information is leading organizations to not only reexamine business operational processes but also to think critically about the capabilities their core back-end banking systems provide, making changes and modernizing systems to keep pace.

Automation is key for increasing operational efficiency. In terms of speed, particularly for high-volume tasks like payment processing, automation to real-time speeds may deliver much higher levels of performance. Typically, financial institutions processed transactions in batches on a set schedule. However, it is no longer sufficient for consumers who now want and expect fast access and up-to-the-second information about their accounts.

Today's automation technologies are better able to handle high volumes of data than their predecessors and can be used to enable new business practices like straight-through processing to settle transactions. With this level of performance, driving near-instant settlement and exception handling, businesses, consumers and governments alike can send funds that are immediately available to the payee, and the payers can have real-time confirmation that transactions were completed as intended.

Benefits of operational efficiency

Operational efficiency may also play a role in helping organizations better detect and respond to financial crime. Fraud, money laundering and other financial crimes can be costly – both in terms of the impact to an organization's bottom line and in the time and resources that banks dedicate to investigating and preventing it.

To help combat fraud, many organizations are implementing capabilities that can integrate new types and sources of data and predictive insights more nimbly to support internal and external-facing business processes. This is just one case where building innovative capabilities like machine learning and artificial intelligence on top of the open hybrid cloud using Linux container technologies can help fight fraud.

Efforts for operational efficiency can be hindered by a number of factors, such as the sheer number of transactions and policy exceptions, new or changing regulations, disparate fraud detection systems, and cumbersome manual processes. Automated decisioning technologies may be used to quickly and consistently evaluate transactions against set business or regulatory policies. While predictive insights can help identify potentially suspicious activities that warrant closer investigation, automated decisioning can direct those cases to the appropriate domain investigators.

Another area where organizations can focus their operational efficiency efforts is managing risk. Within capital markets, for example, constantly changing market conditions and regulatory requirements can hinder the firm’s ability to take action quickly. Pricing and risk calculations that depend on massive amounts of data can be slow, and end up defining business processes. This is at odds with modern banking that wants to move more quickly.

Risk analysts want to be able to quickly and effectively change business rules, access data from multiple sources, and make complex, compute-intensive calculations on large volumes of data. They want to be able to take advantage of embedded insights that can drive automated decisions, integrate a variety of different data types, and apply open source AI/ML and cognitive models that can help automate and reflect complex business processes.

Financial services organizations have a variety of problems and challenges they are faced with on a day-to-day basis, including modernizing payments, detecting fraud and mitigating risk - all while under pressure to minimize the time and cost of operations. Red Hat's open hybrid cloud portfolio helps financial services organizations meet the needs of a new generation of customers, and as a result, can assist your organization in achieving greater levels of operational efficiency. Learn more about Red Hat's operational efficiency solution and see how you can drive greater efficiencies throughout your organization while still delivering innovative, responsive and effective customer experiences.