In the rapidly evolving digital economy of today, financial services organizations need to protect themselves against a rising tide of risks – which now come in an ever-increasing variety of forms. Old, manual methods of risk management are no longer enough. This is where hyperautomation comes into the picture. Hyper Automation, as an advanced intelligent automation strategy, enables financial institutions to proactively identify, prevent, and respond to risks in real time through a combination of RPA, AI, ML, and process orchestration.

Through a reliable hyper-automation services provider, banks and financial institutions would be able to transform from static risk controls to dynamic, predictive systems that cut losses, improve compliance, and secure future success.

What Is Hyper Automation?

Hyper Automation is a business practice that is rapidly identifying, vetting, and automating as many of the business and IT processes as possible – including those that already have been automated with RPA – with technology including RPA, AI, ML, process mining, and low code. While traditional RPA is siloed and takes care of single tasks, hyper-automation bridges tools to model entire end-to-end processes.

Gartner states that hyper-automation is one of the leading strategic technology trends for financial services, which can increase agility, augmenting intelligence in high-risk areas like fraud detection and regulatory compliance.

Hyper Automation in Finance is Becoming More Valuable Than Ever

The finance vertical is experiencing rapid digital transformation, with hyper-automation playing a key part in risk management. Key statistics reinforce this point:

  • The global market hyper-automation in banking was valued at $745.4 million in 2021 and is projected to reach $7.13 billion by 2031, progressing at a CAGR of 25.7%.
  • By 2025, it will account for 35% of entirely automated finance, up from 19% in 2022.
  • AI and hyper-automation are also estimated to give banks a profitability uplift of almost $170 billion over five years.

As risk management practices continue to evolve, many institutions have started relying on RPA companies and Custom RPA development services to develop systems that are more resilient, intelligent, can scale, and cope with complexity and uncertainty.

Powering Risk Management with Hyper Automation

Hyper Automation changes the way banks monitor and mitigate risks from fraud, compliance, operations, and lending. Here are the key ways it matters:

1. Real-Time Fraud Detection

Financial fraud is getting more and more creative. Conventional mechanisms tend to follow rule-based detection, which results in a high number of false positives and ignored threats.

Hyper Automation supports monitoring transactions in real-time with AI algorithms that have been taught to look for anomalies, for example, sudden patterns of large transfers or geographical inconsistencies. For example, banks have been able to achieve the following by automating alerts on suspicious activity and incorporating predictive analytics:

  • 50% decrease in false positives.
  • Quick fraud turnaround—within milliseconds (not really)— Mind you, we did have some fraud issues.
  • Boosted anomaly detection in all channels (online, mobile, ATM)

2. Predictive Risk Analytics

What is truly radical about hyper-automation is that we can now move from a reactive mode of risk management to a predictive one. Data from a variety of sources — transaction history, credit reports, news feeds, behavioral data — can be slurped into machine-learning models to predict potential defaults or liquidity risk.

This allows banks to:

  • Real-time credit exposure control.
  • Warn of deteriorating credit quality before an actual default.
  • Suggest what should be done (E.g. re-negotiate loans).

3. Compliance & risk management regulatory

This kind of control is very resource-intensive and constantly moving. Manual KYC, AML, and reporting operations are both inaccurate and labor-intensive.

With hyper-automation, banks can leverage intelligent document processing to extract and validate identity information from identity documents, automate compliance workflows, and retain audit trails. Benefits include:

  • Up to 70% faster onboarding.
  • Automated scoring of risk for AML validation.
  • Audit logs are ready for regulatory inspections to examine at any time.

4. Operational Risk Reduction

Hyper Automation also takes away human mistakes by automating repetitive, rule-based tasks. For instance, the automation of reconciliation, payment verification, and reporting guarantees the reliability of the course.

Automation QA testing makes sure that each API call, bot, or AI model is monitored and verified, so each move is a verified step toward its goal of reaching the tens of millions! This minimizes process errors, accelerates deployment, and guarantees resilience.

Case Study Highlights

JPMorgan Chase

JPMorgan used AI and hyper-automation tools to simplify its fraud detection, risk modeling, and credit decisions. As a result, the bank:

  • $1.5 Billion in Annualized Cost Savings Realized.
  • Better fraud prevention accuracy.
  • Known for boosting revenue by more than 20% in its asset management division.

Barclays

Barclays hyper-automated with bots that anticipated IT crashes and automatically redirected transactions after a significant systems outage. This decreased downtime by 40% and kept important systems working as they should.

Selecting a Provider of Hyper Automation Services

To do hyper-automation justice in risk management, financial services organizations must select their partners carefully: partners with deep expertise. What to look for in a Hyper Automation Services provider?

  • End-to-end automation: From RPA to AI orchestration.
  • Bespoke RPA: Bespoke solution for specific workflows.
  • System Integration: Make the old new again. Memories are not the only thing that lasts, so do legacy applications.
  • Powerful automation QA testing: To ensure the security and reliability of the apps we have their QA testing done!
  • Financial and Risk domain knowledge: Knowledge of regulation and industry issues

Big RPA companies such as UiPath, Blue Prism, and Automation Anywhere all now have AI modules, but a lot of firms like the idea of bringing in consultants to build their own bots and integrate them into their core banking systems.

Best Practices for Applying Hyper Automation to Risk Management

1.Begin with Quick Wins: Find out high-volume, rule-based work, for example, KYC checks or loan origination.

2.Establish a COE: A specialized team provides governance, scale, and best practices for automation.

3.Prepare the Key Ingredient: Clean, Organized Data for AI and ML Most importantly, clean, organized data is the lifeblood of accurate AI and ML.

4.Work Across Functions: Risk, compliance, operations and IT should share ownership of the automation roadmap.

5. Continuous Monitoring & Improvement: Track KPIs, risk alerts, and adherence to SLAs with dashboards.

Behind The Scenes: Internal Strategy & Future Outlook

As technology develops, hyper-automation will usher in increasingly sophisticated tools, such as generative AI and agent-based systems. 

The next- generation of risk management is proactive, intelligent, and automated. With hyper-automation, financial institutions can secure their base and create a competitive advantage now.

Final Thoughts

As risks continue to grow, financial institutions are increasingly dealing with fraudulent activities, cybercrime operational problems, and compliance transgressions. The old tools are not up to the speed and complexity of the modern risk environment.

A breakthrough another way of going about it is hyper-automation. RPA companies offerings, custom RPA creation, and automation QA testing encapsulate a cohesive risk management strategy by which the bank stands in fast response to the danger, ultimately generating less loss while forging a more agile, compliant, and smarter enterprise.

By teaming up with an experienced hyper-automation services provider, you can have everything you need to develop a successful approach to the current complexities of risk management and ultimately succeed in this era of digital transformation.