Automation In Financial Services Is Shifting to The Front Office

automation in the front office is becoming more prevalent.

In the dynamic landscape of technological advancements, the evolution of automation has been a transformative force. Automation has reshaped industries and redefined the nature of work. Over the past decade, a shift has occurred as automation programs transition from back-office operations to front-office workers. This is having a particularly strong impact today in the financial services and insurance industries. Organizations operating in these industries have a challenging task to manage this transition. There are several reasons, but the most impactful is how it will change the roles of many employees. Those responsible for implementing automation in Financial Services companies need to carefully plan this transition to minimize disruption.

A Historical Perspective: The Financial Services Industry

The financial services industry is no stranger to automation. Investment in ATMs was an early adoption of how deposits and cash withdrawals could be automated. From those early beginnings, much has been done to streamline how processes are executed. Cybersecurity measures are now highly complex and automated applying advanced technology to keep customer funds secure. Over the past ten years, much automation in financial services has been focused on either repetitive tasks or performance improvement.

  1. Repetitive Task Automation: The financial services industry witnessed a surge in automated processes between 2010 and 2020, with a focus on repetitive tasks. For instance, routine data entry and reconciliation processes, which were historically labor-intensive, saw automation through Robotic Process Automation (RPA). This allowed organizations to streamline their operations, reduce errors, and enhance efficiency. Learn more by reading this article, When You Should Consider Business Process Automation vs. RPA.
  2. Performance Augmentation: As the decade progressed, financial institutions sought ways to augment employee performance. Algorithmic trading platforms emerged, leveraging artificial intelligence (AI) to analyze market trends and execute trades at speeds impossible for human traders. This not only increased the efficiency of trading but also mitigated risks associated with human errors.

A Historical Perspective: The Insurance Industry

The insurance industry has also experienced considerable investment in automation technologies. The computerization of data entry and claims processing streamlined operations to improve efficiency with automation. The use of high-powered computer systems did much to help with risk management. Just like the banking industry, investment in automation technologies in the insurance industry followed a similar path. Over the past ten years, they were focused on either streamlining repetitive tasks or performance improvement.

  1. Repetitive Task Automation: In the insurance sector, routine underwriting processes were automated during this period. Data extraction and verification tasks, traditionally done manually, were expedited through automation tools. This not only accelerated policy issuance but also reduced the chances of errors associated with manual data entry.
  2. Performance Augmentation: Insurance companies started utilizing AI-powered chatbots to enhance customer service. These virtual assistants, equipped with natural language processing capabilities, could handle routine queries, leaving customer support teams free to focus on more complex issues. This marked a significant shift in how front-office tasks were managed. Learn more by reading this article, 5 Ways RPA Is Changing the Title Insurance Industry.

The Next Level of Automation in Financial Services and Insurance

As we look to the future, it is now expected that the focus on investing in new automation in financial services will have a greater impact on workers in the front office. In other words, the higher value and industry transformation automation projects will shift away from back-office operations. These types of automation projects will be different so require new change management strategies.

For example, a well-known automation in financial services strategy is the implementation of Robotic Process Automation (RPA). RPA bots can efficiently handle repetitive data entry and reconciliation tasks, ensuring accuracy and freeing up human resources for more strategic and value-added activities. Identifying repetitive tasks is relatively straightforward. Configuring RPA bots requires careful consideration of process intricacies and potential system integration challenges. These types of automation projects remove repetitive, mundane tasks from front office workers – a welcome improvement.

Future automation in financial services strategies has the potential to provide enhanced decision support. This type of change will require employees to shift how they work. Instead of relying on a familiar decision process or gut instinct, new processes might instead run on an AI inquiry. As these types of process improvement are implemented, front office worker productivity will further increase. Greater knowledge will also be available to address a wider scope of issues. Combined, these types of changes will make a pronounced impact on how technology will disrupt how employees do their jobs.

Automating Enhanced Decision Support:

Here are a couple of use-case examples of how new challenges can be overcome by implementing more advanced automation and AI technologies.

Example: Financial Services Industry

  • ChallengeFinancial institutions are inundated with vast amounts of complex financial data. Analyzing this data manually for informed decision-making is not only time-consuming but also prone to oversight, hindering the ability to identify patterns and trends that could inform strategic decisions.
  • Financial Services Automation SolutionThe implementation of AI-powered analytics tools addresses this challenge by automating the analysis of financial data. These tools can quickly process large datasets, identify patterns, and generate actionable insights, empowering decision-makers to make more informed and strategic choices.
  • DifficultyHigh: Developing and deploying advanced analytics capabilities requires a skilled team of data scientists, a robust understanding of financial data structures, and ongoing refinement to ensure the accuracy and relevance of insights.

Example: Title Insurance Industry

  • Challenge Optimizing underwriting decisions and assessing today’s more complex policies accurately requires a more comprehensive analysis of a multitude of factors. Traditional underwriting processes may fall short in evaluating complex risk scenarios, leading to suboptimal decisions.
  • Automation Solution The integration of predictive analytics models into underwriting processes enhances decision support by leveraging historical data and sophisticated algorithms. This enables insurers to assess risk more accurately, optimize underwriting decisions, and improve overall underwriting efficiency.
  • Difficulty High: Developing predictive analytics models involves complex statistical modeling, data preprocessing, and ongoing refinement to ensure the models remain effective in evolving market conditions.

Understanding and strategically applying tomorrow’s more advanced levels of automation will require new skill sets and change management skills. This transformation will disproportionately impact front-office operations. Success will require greater employee engagement which is different than traditional automation projects.

Those responsible for these projects in the financial services and insurance industries must not only navigate challenges but also seize opportunities for innovation and growth. By carefully considering the challenges, selecting appropriate automation solutions, and acknowledging the level of difficulty associated with implementation, businesses can embark on a transformative journey that redefines the efficiency and effectiveness of their front-office operations.

Through collaboration with experienced systems integrators such as Axis Technical Group, these organizations can ensure that their automation initiatives are not only successful but also sustainable, propelling them into a future where technology and human ingenuity work seamlessly in tandem.