The hyperautomation It’s not just a technological promise; it’s a proven strategy for scaling operational efficiency in high-volume organizations. In this case study, we explore how a global financial services company implemented a hyperautomation initiative that transformed its critical processes, reduced costs, and accelerated decision-making across the organization.
Profile of the company
A multinational with operations in more than 30 countries, specializing in financial services and banking products. With more than 50,000 employees, it faced growing challenges in operational efficiency, regulatory compliance, and customer experience. Its ecosystem included legacy systems, multiple ERPs, and highly manual processes in key areas such as risk, back office, and customer service.
Initial challenges
- More than 4,500 documented processes, 65% of which were executed manually
- Average transaction processing times exceeding 48 hours
- High operational risk due to human errors in repetitive tasks
- Difficulties in meeting SLAs in regional operations
- High operating costs and limited visibility into process productivity
Project objectives
- Automate critical back office processes in less than 12 months
- Reduce operating costs by at least 25%
- Increasing the speed and traceability of regulatory and transactional flows
- Improving the end-customer experience in service and response
- Strengthening the governance of processes and real-time data
Implementation strategy
The company adopted a progressive approach to hyperautomation, combining RPA, AI, process mining, and BPM. A 18-month roadmap was structured, with measurable deliverables every quarter.
1. Diagnosis and process mining. Process Mining was used to identify processes with the highest friction, volume, and cost of errors. This layer of visibility enabled mapping of interactions between systems, critical manual tasks, and opportunities for immediate automation.
- Automation with RPA and intelligent bots. Banking reconciliation processes, policy processing, risk analysis, and document validation were prioritized. OCR and AI bots were implemented to read forms, validate data across multiple systems, and generate automated reports.
- Integration of AI and machine learning. ML models were included for fraud prediction and the prioritization of complex cases, allowing the bots to make automated decisions in 75% of the operations.
- Orchestration with intelligent BPM. Automated workflows with iBPMS systems were developed to coordinate processes between departments, integrating humans and bots with complete traceability and real-time monitoring.
- Governance and scalability. KPIs were defined, continuous monitoring policies were established, and an automation excellence center (CoE) was established, which operated as an internal factory for bots and process optimization.
Results obtained in 12 months
- 60% reduction in the processing time for critical tasks. The processing of reconciliations was reduced from 48 to 6 hours. Risk validations were reduced from days to minutes.
- 32% reduction in operational costs in automated areas. Thanks to the elimination of manual tasks, optimization of workflows, and reduction of errors.
- 98% precision in document processes through AI. With OCR and NLP integrated, human errors in tasks such as policy and contract validation were eliminated.
- +70% improvement in compliance with regulatory SLAs. The traceability of processes and the generation of regulatory reports with intelligent alerts were automated.
- +45% customer satisfaction in automated channels. Response times in customer service were reduced and an intelligent digital self-service was enabled.
Key factors of success
- Stakeholder participation from IT to business areas
- Clear visibility of the financial impact of each automation
- Robust governance with clear roles and policies for bots and AI
- Internal training to enable a data-driven and automated culture
- Scalability from pilot to 10 global regions in less than a year
Lessons learned
- Process automation without mining can automate inefficiencies
- Integrating AI from the outset allows value to be scaled beyond basic RPA
- Not everything needs to be automated: choosing the right process is more cost-effective than trying to cover everything.
- Alignment with business goals is what ensures sustainability and expansion
Organizations with complex operations and high transaction volumes. With a data-driven approach, integrated technologies, and robust governance, it is possible to radically transform efficiency, reduce risks, and improve the customer experience on a global scale.
According to Forrester, Leading automation organizations can save between $5M and $20M USD annually by scaling critical processes with AI and RPA [Forrester Research, 2024].
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