Key Considerations for Optimizing Banking Operations 


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As client expectations for a seamless and personalized experience become more prevalent, traditional banks find themselves at an inflection point. While increasing competition from B2C fintech companies and the breakneck pace of technological innovation threaten the leadership of incumbent banks, these banks still have a considerable advantage due to their large existing client base. These incumbent banks can solidify their foothold — as well as extract valuable insights from customer data, grow wallet share with their existing base, and accelerate growth while maintaining compliance with regulatory and risk standards — by modernizing operations.  

Although many traditional banks have found some success in modernizing core systems and embracing new industry trends, progress has come in fits and starts — certainly not enough to regain enduring market leadership. To do so, they will need to achieve sustainable operational efficiency in banking, which requires a holistic, yet phased and iterative, approach.  

The pressure to realize success with operational efficiency puts leaders in a difficult position, forcing them to navigate a complex market environment, to meet customer demand, and to control costs all while simultaneously driving growth. At present, there are so many advances in technology, and the path to operational excellence in banking is not always clear.  

We’ve outlined some key considerations and ideas for banking leaders to factor into their journey towards operational efficiency.   

What Defines Operational Excellence in Banking? 

Although every institution’s idea of what operational excellence looks like will vary, the general consensus is that it requires delivering high-quality service and exceptional customer experiences while minimizing costs and risk.  

As a result, a successful banking operations strategy should meet the following criteria:  

  • Customer-centric: Banks that achieve operational excellence consistently prioritize customer needs and preferences. To do so, they must have an in-depth understanding of their audience based on actual customer data and real-time analysis. Hyper-personalized experiences should seamlessly span multiple channels and cater to each customer’s individual needs.  
  • Optimized: To achieve operational efficiency in banking, institutions must continuously iterate and improve upon existing processes to enhance their accuracy, efficiency, and speed. This calls for analyzing workflows and removing unnecessary steps, identifying and eliminating bottlenecks, and automating manual processes to save employees time and effort. When seeking to optimize processes, it is important to consider the requirements of all end-users across multiple departments, including front and back office, legal, compliance, and more.  
  • Secure: Fraud, cyberattacks, data breach, and other forms of risk all pose a serious threat to operational excellence in banking. A single security breach can bring operations to a standstill — not to mention the long-lasting damage inflicted on an institution’s reputation and on customer trust — so it’s imperative that banks establish robust risk assessment frameworks, implement stringent compliance measures, and employ advanced analytics to identify and mitigate risks.  
  • Technologically Savvy: One of the keys to achieving operational efficiency in banking is to leverage cutting edge technology, such as artificial intelligence (AI), machine learning, predictive analytics, and robotic process automation (RPA). With that said, banks should not blindly invest in new tools simply because they’re trendy, as this can lead to poor adoption, resource waste, and a negative ROI. Tech-related investment decisions should be made strategically in order to meet actual business needs, resolve actual pain points, and yield tangible benefits.  
  • Agile: Given the current pace at which the banking industry is evolving — where even the smallest change feels more like a sea change — financial institutions need to the ability to pivot and adapt to new market trends, technologies, and ways of thinking and doing business. This requires agile operations, which rely upon cross-team and cross-functional collaboration, data-driven decision-making, scalable systems, and an organization-wide culture of continuous innovation and improvement.  

Major Obstacles to Optimized Banking Operations 

To achieve long-term, sustained operational excellence in banking, operations leaders have significant hurdles to overcome — hurdles such as: 

  • Legacy Systems: Legacy core systems present a serious challenge for financial institutions looking to optimize banking operations. Legacy systems are often incompatible with newer technologies, have limited automation capabilities, are built on rigid architecture, and lack the flexibility and agility banks need to quickly adapt to changes in the marketplace. Though modernization is a worthy — and, one might argue, essential — endeavor, it can take significant time and resources to achieve. 
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  • Data Silos: One of the primary issues with relying on legacy systems for business-critical operations is a lack of interoperability. Without being able to integrate disparate systems, data can easily become fragmented, creating data silos. Data silos can also develop when different departments are responsible for managing various data sets and don’t have a way to easily share them with one another. These silos can impede data accuracy, analysis, and reporting, which can, in turn, prevent banks from making data-driven decisions to enhance operational efficiency.  
  • Intricate Workflows: In banking, a single transaction can touch multiple teams and systems, require various approvals, and be subject to strict compliance procedures. This degree of complexity for even the simplest of workflows can make it difficult for banks to implement streamlined and standardized processes, which impedes efficiency. 
  • Changing Regulations: Banks operate in a highly regulated environment. From anti-money laundering (AML) and know your customer (KYC) regulations to the Dodd-Frank Act, the Sarbanes-Oxley Act, and the Gramm-Leach-Bliley Act, the list of laws and regulations to which financial institutions are subject is truly extensive. Compliance efforts can be costly and add an additional layer of complexity to operations, as banks must constantly invest in new systems, implement new processes and protocols, and add personnel to ensure compliance with ever-changing regulations. 
  • Cybersecurity Threats: As technology becomes more sophisticated, so do the methods that fraudsters and hackers use to gain access to the data of banks and their customers. This poses a problem for operations leaders, who must ensure that all processes and workflows include rigorous security protocols to safeguard sensitive information and comply with data privacy regulations.  
  • Resistance to Change: Sometimes the greatest obstacles to operational excellence in banking are financial institutions’ employees. Banks can implement new solutions or more efficient workflows but, at the end of the day, without buy-in from the people responsible for utilizing those solutions or following those workflows, they’re unlikely to see meaningful change. Therefore, any strategy intended to drive operational excellence in banking should assess the impact on employees and create a change enablement plan to encourage adoption and adherence.

How to Drive Operational Efficiency in Banking 

For financial institutions seeking a solution to optimize banking operations, data analytics and artificial intelligence are two key enablers. 

Data Analytics 

Let’s look at the applications for data analytics in banking operations, first.  

One of the most significant areas in which data analytics can help streamline operations is risk management. Banks can apply advanced analytics techniques, such as predictive analytics and machine learning models, to historical data to set baselines for customer and market behavior. Once they have a clear picture of what “normal” looks like, they can analyze real-time data to identify any anomalous behavior that could point to fraudulent activity, market volatility, or other forms of risk and take proactive measures to limit risk exposure and prevent financial losses.  

Data analytics also plays a crucial role in optimizing customer-facing operations, including creating more personalized, connected customer experiences. Banks can gain a comprehensive understanding of customer behavior, preferences, and needs, all by analyzing customer data. Armed with this information, banks can segment customers, create personalized offerings and marketing campaigns, enhance cross-selling and upselling strategies, and even proactively reengage customers at risk of churning.   

Data analytics can even support regulatory compliance. Banks and other financial institutions are subject to a wide variety of financial and regulatory reporting requirements. Historically, it’s taken banks significant time and effort to aggregate and analyze the data needed to generate these reports. With data analytics, they can automate data collection, validation, and analysis, making the process much easier and more efficient, all while ensuring regulatory compliance. 

The greatest benefit data analytics offers from an operational standpoint, though, is workflow optimization. Financial institutions can analyze existing processes and workflows to identify bottlenecks that could impede operational efficiency and manual tasks that could be automated. Banks can also leverage these insights to make data-driven decisions around resource utilization, potentially saving time and money.   

Consolidating data lake, data engineering, and data engineering capabilities within a single platform is a powerful and efficient way for banks to take full advantage of data analytics and optimize operations. Going forward, generative AI will play an essential role in helping traditional banks ingest, process, and analyze large data sets and reveal valuable insights to further maximize efficiency.

Artificial Intelligence 

Banks can leverage many different varieties of artificial intelligence (AI), including generative AI, to enhance operational efficiency across a wide range of processes and workflows — starting, once again, with risk management, as a use case. The machine learning models used to analyze complex historical and real-time datasets are a form of AI, one that banks can use to assess risk, detect fraudulent activity, and mitigate operational, compliance, and security risks. 

One of the biggest selling points of AI for banking operations is automation. The loan underwriting process is an excellent example: AI algorithms are capable of analyzing vast quantities of customer data, including financial records, credit history, transaction history, and more to assess a customer’s creditworthiness. Based on this information, banks can make more accurate lending decisions and expedite the approval process, reducing their risk exposure and the amount of time it takes to evaluate loan applications.  

By using AI to automate loan underwriting, financial institutions can achieve efficiency gains while maintaining rigorous risk assessment standards. The same is true for a wide variety of processes or workflows, including customer onboarding, transaction processing, loan servicing, data entry, compliance monitoring, and more. It’s clear that AI has the potential to radically transform how banks operate.  
Another highest value area where AI can make a significant operational impact is customer service and support. Financial institutions can implement AI-powered enterprise chat or virtual agents to address routine customer inquiries, provide personalized service, deliver round-the-clock support, and escalate issues to live agents as needed. By automating customer service interactions, banks can reduce wait times and enhance customer satisfaction without having to add headcount to existing service teams. And as AI has recently become more advanced, virtual agents are now able to handle increasingly complex requests — all while using natural language — further decreasing the burden on live support agents.   

Last, though certainly not least, AI has the power to increase workplace productivity by making bank employees better and more efficient at their jobs. Financial institutions can use AI to create internal virtual assistants to support their service and sales teams, answering common questions, providing them with data visualizations, creating new productivity-boosting applications with minimal coding experience, and helping them to maintain governance and compliance.

As generative AI continues to evolve and its capabilities become more advanced, we can expect banks to apply it to everything from automating key aspects of customer service and creating personalized marketing strategies to analyzing complex data sets and proactively detecting risk.  

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Next Steps: Key Considerations for Optimizing Banking Operations 

Data analytics and AI provide a solid foundation for operational excellence in banking and unlock valuable insights from customer data, a key differentiator for traditional banks. However, as we’ve established, simply investing in technology on its own isn’t the answer. In order to optimize banking operations, operations leaders require a solid strategy grounded in the end-user experience.  

It’s important to consider which groups will be impacted by your organization’s modernization efforts and to include them — or, at the very least, take their perspective into account — at the appropriate steps in the process. When developing a strategic roadmap, account for company priorities, user requirements, client experience, compliance obligations, risk management tactics, and potential trade-offs relative to effort and outcomes. These aspects are crucial not only to improving operational design, but also realizing the benefits of operational modernization. 

For over a decade, Hitachi Solutions has helped banks, credit unions, and other organizations in the financial services industry leverage the full power of the Microsoft platform to drive operational excellence. Our technical expertise, signature advisory engagement approach ,and proven industry experience make us the ideal fit for any optimization initiative.  

Contact us today to learn more.