Automation in the financial services industry refers to the use of technology to perform tasks with minimal human intervention. It aims to increase efficiency, reduce errors, and improve service delivery. Automation ranges from simple, rules-based processes to more complex systems involving artificial intelligence (AI) and machine learning (ML). Firms should follow a structured automation strategy for financial services to ensure a steady progression to higher levels of performance. This roadmap provides more significant benefits as firms advance. PricewaterhouseCoopers (PwC) has defined this automation maturity model with five key stages (source). Each stage builds on the previous one, culminating in autonomous intelligence, which enables predictive capabilities and advanced decision-making.
Let’s take a look at each of these stages to identify the level of automation maturity at your organization.
Stage 1: Rules-Based Automation
Rules-based automation is the simplest form of automation. In this stage, predefined rules trigger processes based on specific inputs or actions. The system follows a “if X happens, then do Y” approach.
For financial services firms, this may include:
- Automating customer data entry based on forms submitted.
- Processing loan applications triggered by completed customer documents.
- Automated email responses to customer inquiries.
At this stage, automation is limited but provides immediate benefits such as faster response times and reduced manual effort. However, it’s mainly focused on repetitive tasks with clearly defined rules. Establishing this basic level is a critical starting point in an effective automation strategy for financial services.
Stage 2: Robotic Process Automation (RPA)
Robotic Process Automation (RPA) is the next level of automation. It focuses on automating repetitive tasks through software bots that mimic human actions. These bots can interact with multiple systems, perform data entry, and handle tasks without human intervention.
Examples of RPA in financial services include:
- Using bots to handle routine, web-based customer service inquiries.
- Automating account reconciliation tasks.
- Processing claims and insurance data faster.
At this level, RPA reduces operational costs and human errors by automating high-volume, low-complexity tasks. This stage is key to advancing any automation strategy for financial services, though RPA still lacks the intelligence to make decisions independently.
Stage 3: Process Orchestration
Process orchestration expands automation across various applications and functional areas. It involves a rethinking of existing processes to enable more holistic automation. In this stage, firms begin to integrate their systems and automate workflows across departments.
Examples of process orchestration include:
- Automating end-to-end mortgage processing, from application to approval.
- Streamlining customer onboarding by integrating several platforms (e.g., KYC, AML, and CRM systems).
- Coordinating between front, middle, and back-office functions, such as client reporting and risk management.
Process orchestration significantly improves efficiency, cost savings, and overall productivity. It serves as an essential step in an automation strategy for financial services, where firms rethink and optimize their workflows.
Stage 4: Intelligent Automation
Intelligent automation adds cognitive technologies to automation workflows. This includes AI, machine learning, and advanced analytics. These technologies enable the system to “learn” from data and improve over time.
For financial services firms, intelligent automation might involve:
- Using AI-powered chatbots to provide personalized customer service.
- Applying machine learning to detect fraudulent transactions in real-time.
- Automating investment portfolio management using predictive algorithms.
Intelligent automation represents a major leap forward in an automation strategy for financial services. It allows for greater decision-making capabilities, advanced insights, and enhanced customer experiences. However, it requires a solid data infrastructure to support AI and machine learning models.
Stage 5: Autonomous Intelligence
The final stage of automation maturity is autonomous intelligence. In this stage, systems not only learn from data but also make decisions based on that intelligence. Autonomous systems can act without human intervention and may even predict future events or behaviors.
Examples in financial services include:
- Predicting market movements to adjust investment portfolios autonomously.
- Automating compliance checks based on changes in regulatory environments.
- Creating dynamic pricing models that adjust in real-time based on customer behavior or market conditions.
Autonomous intelligence is the pinnacle of any automation strategy for financial services. It empowers firms to move from reactive to predictive decision-making, staying ahead of market trends and customer needs. This final stage is where the full potential of automation is realized.
Sequential Progression in Automation
Each stage of automation maturity builds on the foundation of the previous one. Without mastering rules-based automation, firms cannot effectively implement RPA. Similarly, intelligent automation requires process orchestration to ensure integrated workflows are in place.
For instance:
- Rules-based automation in processing customer data lays the groundwork for RPA to handle repetitive inquiries.
- Once RPA is established, process orchestration ensures that different departments can work together seamlessly.
- Only after these systems are in place can firms apply AI and machine learning for intelligent automation and to amplify performance.
- Finally, the autonomous intelligence stage requires all previous stages to be optimized to enable predictive decision-making.
Attempting to skip stages or implement advanced technology without the proper foundation can result in inefficiencies, errors, and reduced benefits. Therefore, it is critical to follow this sequential progression when developing an automation strategy for financial services.
Key Metrics for Measuring Automation Success
To assess progress along your automation maturity path, you should establish key performance metrics to measure success. These metrics ensure that automation is providing the intended benefits at each stage. Examples of key metrics include:
- Cost Savings: Track reductions in operational costs due to automation.
- Error Rates: Measure the decrease in manual errors as automation increases.
- Process Speed: Monitor how much faster processes are completed through automation.
- Customer Satisfaction: Evaluate customer feedback to determine if service levels improve with automation.
- Employee Productivity: Assess how much more work employees can handle as repetitive tasks are automated.
- Regulatory Compliance: Track how effectively the firm meets regulatory standards using automated systems.
Each step of the automation strategy for financial services leads to varying levels of performance improvement. For example, rules-based automation and RPA may deliver moderate cost savings and faster process times, offering a solid foundation for more advanced automation stages. As firms progress to process orchestration, they can expect more substantial improvements in both productivity and error reduction, as workflows become more integrated and efficient across departments. Finally, intelligent automation and autonomous intelligence provide significant competitive advantages, allowing firms to optimize workflows in real-time and make predictive decisions that enhance overall business agility and foresight.
The Benefits of Working with a Subject Matter Expert
Navigating the automation journey can be complex, especially for firms with legacy systems or limited internal expertise in AI or machine learning. Working with an experienced partner can provide several key advantages:
- Expertise: Partners bring specialized knowledge in implementing automation strategies tailored to the financial services industry.
- Accelerated Implementation: Automation solutions can be deployed faster when guided by a partner, ensuring firms achieve benefits sooner.
- Reduced Risk: Partners help minimize risks by ensuring that the right infrastructure and governance are in place at each stage of automation.
- Cost Efficiency: Although working with a partner requires an initial investment, the incremental cost savings and improved performance achieved through automation will likely cover those costs. A partner can help fix errors sooner in the deployment, helping to save costly mistakes that require substantial rework later in the process.
Collaborating with experts ensures that financial services firms can efficiently advance through the stages of their automation strategy for financial services. With the right partner, firms can accelerate their automation journey, gaining faster access to cost savings, enhanced service delivery, and competitive advantage.
Advancing an automation strategy in financial services will achieve greater success when following a structured maturity path. Beginning with simple rules-based automation and progressing to autonomous intelligence, firms can unlock increasing benefits as they advance. Key performance metrics, such as cost savings, error reduction, and productivity gains, should be tracked at each stage. Working with an expert partner makes the journey more efficient, helping firms achieve their goals faster and more effectively. With automation, financial services firms can future-proof their operations and stay competitive in an increasingly digital landscape.