The Role of Banking Analytics in Driving Customer Growth

Financial institutions are drowning in transactional, behavioral, and operations data. Making sense of that data in real time matters more than ever. From an operations efficiency standpoint, banks need to do more with less. From a customer-centricity standpoint, they need to convert data into customer insights for banks in order to cross-sell, mitigate risk, stop fraud, and deliver personalized experiences. Enter hyper-automation and enterprise automation. Both are strategic technologies that banks will want to use. But they’re not the same thing. This article describes the difference between them, maps both to specific banking use cases, and provides guidance for implementation.

Summary

• Enterprise automation is about automating repeatable tasks and processes at scale for stability and cost savings.
• Hyper-automation is an enterprise-wide business strategy that combines tools and intelligence to automate complex end-to-end processes and enable continuous improvement.
• Most banks will need a combination of both. Enterprise automation creates the foundation for hyper-automation, which builds on that foundation to create smart processes that power real-time banking analytics and customer insights.
• Tools like Hexaware PaymatiX™ take automated data flows and feed them into the analytics layer, where it is enriched, and made available to decision makers in a timely manner.

Definitions, Simply Put

Enterprise Automation

It refers to the widespread automation of business and IT processes across an enterprise. It encompasses robotic process automation (RPA), workflow engines, scheduled jobs, data integration middleware, etc. The objective is to create stable, predictable, and auditable processes that can be run repeatedly to increase throughput and reduce operating cost. Enterprise automation covers tool-agnostic services like managed runbooks, incident automation, and business process automation (BPA).

Hyper-automation

Hyper-automation combines several technologies, including robotic process automation, artificial intelligence, process mining, low-code platforms, data integration fabric, analytics, and more, to automate complex processes from end to end. Hyper-automation is less tool-focused and more about creating an automation ecosystem that both measures results and identifies new opportunities to automate. Hexaware’s hyper-automation platform, Tensai®, and digital transformation methodologies span both IT and business processes.

Key Differences at a Glance

Relationship Between the Two in Banking

Imagine enterprise automation as the pipes that connect all of the systems and processes in a bank. Now imagine hyper-automation as adding smart sensors and intelligent connectors to those pipes — enabling real-time insights from data. Applied to banking:

• Enterprise automation: This includes capabilities such as straight-through processing (STP) of payments, daily batch reconciliations, scheduled reporting jobs, and incident remediation in core systems. These tools remove human interaction from the processes and provide operational stability.
• Hyper-automation: Hyper-automation builds on the consistency created by enterprise automation and connects customer onboarding, KYC processing, fraud scores, credit scoring, and decision engines into an intelligent workflow that can automatically react or provide prioritized human intervention backed by AI models and real-time intelligence. Action can take the form of automatically blocking a transaction or applying real-time banking analytics to detect risky behavior.

Once a bank has hyper-automation and enterprise automation working together, the combined automated data flows can feed into the analytics layer. Modern banking analytics platforms like PaymatiX™ ingest automated data streams, transform them into a normalized structure, and provide customer insights for banks and real-time banking analytics dashboards. PaymatiX™ is one example of a cloud-native banking analytics platform that specializes in retail and payments use cases.

When Enterprise Automation Excels

• Operational stability: Enterprises rely on standardized processes that operations teams know and trust.
• Quick wins: Automating high-volume manual tasks delivers rapid cost savings.
• Manageable scope: As the focus remains on repeatable tasks, governance and risk are easier to define and manage.
• Use cases: Back-office processing and reporting, IT operations automation, batch jobs, and large scale reconciliations.

When Hyper-automation Excels

• End-to-end process optimization: Hyper-automation spans systems, human tasks, and decision points across the enterprise.
• Constant improvement: Hyper-automation platforms incorporate process mining and analytics to identify new automation opportunities.
• Business outcomes: Hyper-automation includes AI/ML to deliver predictions, natural language processing, and dynamic routing/workflows.
• Use cases: Intelligent customer journey automation, dynamic fraud containment, risk management, and personalized banking driven by real-time banking analytics.

Real-world Examples Tied to Banking Processes

Payments Processing

Enterprise automation: Utilize RPA to reconcile failed payments and automate exception resolution.

Hyper-automation: Pair RPA with AI to predict the root cause of payment failures. Route high-risk or high-value cases to humans and automatically correct failures that present low risk. Feed payments processing data into the analytics platform for trending purposes.

KYC and Customer Onboarding

Enterprise automation: Automate intake of forms and routing of supporting documents.

Hyper-automation: Incorporate OCR/ID verification powered by ML, risk scoring, and a decision engine that automatically approves low-risk cases while escalating approvals that require further review. Sending onboarding “signals” or data points into a customer analytics model helps improve cross-selling.

Fraud Detection and Response

Enterprise automation: Automate rule-based alerting and batch investigation processes.

Hyper-automation: Implement real-time streaming analytics that compare behavioral signals to ML models with automatic transaction blocking or holds for further review. RPA can automate remediation tasks downstream. Monitoring real-time banking analytics on dedicated operations dashboards empowers rapid decision-making.

Customer Insights and Personalization

Enterprise automation: Auto-generate monthly customer reports.

Hyper-automation: Ingest customer behavior in real time while also scoring and applying AI-driven propensity models that trigger moments of personalization across digital channels. Tools like PaymatiX™ deliver the visualization and operational layer for these insights and actions.

Components of an Enterprise Automation and Hyper-automation Strategy

Automation Platform/Orchestration

Automation comes in many forms, but having a centralized platform that includes RPA, workflow engines, and APIs/connectors for your core banking systems is important. Hexaware has a hyper-automation platform called Tensai® that helps clients utilize all of these components at scale.

Data Fabric/Analytics

Having a centralized repository and analytics engine is critical. That includes both batch and streaming use cases. Ensure your platform can handle real-time data ingestion, data cleansing, normalization (data fabric), and reporting. PaymatiX™ is a banking analytics platform that provides these capabilities, designed specifically for banking and payments.

AI/ML Models

You’ll need AI models for risk, propensity, churn, fraud scoring, etc. Additionally you’ll need MLOps capabilities to ensure those models stay healthy in production.

Process Mining/Discovery

You need a way to identify automation opportunities. So, use process mining to analyze event logs and highlight process automation candidates. Process mining will also help you measure cycle time while pinpointing processes that will have the biggest impact.

Governance and Center of Excellence (CoE)

Lastly, you need an automation CoE. This will allow you to create standards, measure ROI, and maintain governance standards across the organization. Having a governance and CoE program will ensure your automations are consistent, secure, and easily audited.

Operational Expertise

Don’t forget about the people. You need process owners, data stewards, and SMEs to sign off on your automation designs. Remember that process and people are just as important as the technology.

Analytics and Customer Insight Implications

Clean, automated data flows help improve the quality of analytics and insights you can generate about your customers. How?

• More accurate data: Automated ingestion means fewer manual errors, so you can trust your analytics.
• Faster insights: Automated refresh rates mean you can generate near-real-time dashboards and scores.
• Better signals: Automation creates metadata about processes. Did John Smith’s onboarding encounter exceptions? Was Sally Jones flagged for potential fraud? These types of signals can improve analytics and allow you to trigger personalized offers.

Hyperautomation allows you to close the loop by turning analytics output into automated triggers or actions. Hexaware’s PaymatiX™ platform was built from the ground up to support operational and transactional feeds from automations. The platform can then apply analytics, generate insights, and expose them to stakeholders throughout the organization.

See how PaymatiX™ helped a US bank deliver better customer analytics.

The above case example from Hexaware shows how a US bank was able to consolidate customer data and deploy banking analytics models with visualization using PaymatiX™. With automated processes powering the data feeds into the platform, the bank gained actionable customer insights that drove better decisions.

The Typical Automation Adoption Lifecycle for Banks

Identify and Prioritize

Use process mining to visualize as many processes as possible. From there, you can determine process value and complexity to prioritize work. You should also perform an automation maturity assessment to identify low-hanging fruits.

Establish Foundational Automations

Start with enterprise automation to clean up high-volume repetitive tasks. Once those processes are stable, you can free up capacity to focus on more valuable work.

Implement an Analytics Platform

Introduce a centralized analytics platform to consolidate your data into a single location. From there, you can build out baseline operational dashboards and start exploring opportunities for customer analytics.

Hyper-automate

Next, you should start layering AI, process automation, and dynamic decisioning onto end-to-end processes. This is where your automation CoE and productization, enabled by governance and templates, come into play.

Operationalize for Continuous Improvement

With your automation and analytics layers in place you can start turning analytics into operational triggers and fully closed-loop automations. Continuous improvement should be your goal.

Measurable Business Outcomes to Target

• Increase in straight-through processing rates.
• Reduction in mean time to resolution for exceptions.
• Reduction in fraud detection lead time.
• Incremental revenue from personalized offers.
• Reduction in operating costs from manual interventions.

Hexaware has documented examples of how intelligent process automation and PaymatiX™ have driven measurable improvements in risk detection accuracy and customer analytics outcomes.

Tips and Common Mistakes to Avoid

Tips

• Data first: Your data and analytics platform should be your north star when planning for hyper-automation.
• Process mining: Don’t waste your time automating the wrong things. Process mining will help you visualize and identify automation candidates.
• Focus on reusable automation assets: Create APIs and modular automation assets that can be shared across the organization.

Mistakes to avoid

• Automating poor processes will make them run faster!
• Failure to implement governance and a CoE will lead to security risks down the road.
• Don’t build islands of automation. Ensure your automations feed into a centralized data and analytics platform.

Sample Technology Stack for Banks

• Integration/API layer: Enterprise service bus/API gateway to connect core banking systems, payments systems, CRM systems, etc.
• Automation layer: Robotic process automation, workflow engines, and orchestration platforms. Tensai® fits into this category at scale.
• Data platform: Cloud native data lake, streaming data ingestion, and a centralized analytics platform like PaymatiX™.
• AI/ML: A model training and deployment platform with MLOps capabilities built in.
• Governance: Automation center of excellence, logging, role-based access control (RBAC), audit trails.

Recommended Approach: Use Both, but Do Them in Order

Banks shouldn’t view hyper-automation and enterprise automation as competitors. Instead, think of hyper-automation as the intelligence and enterprise automation as the plumbing that powers it. First, banks should focus on using enterprise automation to stabilize operations and build out a robust data and analytics layer. With that foundation in place, banks can start hyper-automating by tying intelligent processes together and reacting to real-time analytics. Products, such as PaymatiX™, paired with automation platforms like Tensai® accelerate hyper-automation at scale for our customers.

Conclusion

Enterprise automation and hyper-automation are not competitors. Enterprise automation is essential plumbing. Hyper-automation is the intelligence and orchestration that leverages the plumbing to deliver adaptive, outcome-oriented workflows. To obtain real-time banking analytics and richer customer insights for banks, a combined approach is the right one. Deploy a banking analytics platform, clean and stream your operational data, and then apply hyper-automation to close the loop between insight and action.