Automation in Risk Management: A Complete Overview

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Running a business comes with risks—circumstances or events—that may threaten your company's ability to continue operating.

Changing market conditions, breakthrough technology, encumbered operations, increased data availability, new value chains or business models, and evolving regulations transform how you operate your business, serve customers, and interact with third parties.

Risk management helps mitigate the uncertainty this causes.

But how do you manage all these risks in a dynamic business environment? Emerging automation technologies like robotic process automation (RPA), artificial intelligence (AI), and machine learning (ML) may help.

Organizations that automate risk management can identify potential risk events in real-time, strengthen controls, reduce errors and expenses, and improve efficiency.

This guide shares how to proactively plan for and automate risk for a more secure and profitable business.

What is risk management automation?

A professional uses a laptop to automatically manage risk.

Risk management involves applying resources to identify, assess, manage, and control the impact of business risks on a company's capital and revenue.

Automating risk management provides real-time visibility into the process, giving you valuable insights into potential or existing risks. Plus, it prevents mistakes that lead to financial catastrophes.

At its most basic, risk management automation streamlines tasks once handled by people—but doing much more.

For instance, conducting an internal audit—a traditionally labor-intensive and error-prone exercise—requires sifting through volumes of data.

Automation analyzes patterns in the data, spots potential problems in real-time, and makes predictions. It's revolutionizing how companies think about risk.

The need for risk management automation

Risk professionals discuss the automation they're using for assessments.

When the pandemic hit, companies from every geography and industry discovered that their disruptive risk management processes were outdated.

Businesses that relied on point-in-time assessments to monitor risk struggled with several blind spots as data quickly became stale and useless for decision making.

Human employees can't monitor risk continuously. Instead, effective risk management requires a better way of providing ongoing risk intelligence and continuous monitoring.

Traditional risk management models had four main issues:

Human involvement: While employees reduce the risk and compliance required, they can make errors or even forget to report compliance.
Errors: When conducting monotonous tasks or overworking, employees are more likely to make errors, resulting in higher risks.
Lack of coordination: Inefficient coordination and silos between human workers make it harder for businesses to meet their goals and objectives.
Unoptimized resources: Unlike smart technologies, which respond to issues quicker, human employees can't work fast to address underlying risks or problems.

That's why organizations want automation. Without it, companies will have to burden their risk management teams with an increased workload resulting from continuous monitoring.

In fact, Deloitte's 12th Global Risk Management Survey found 30% of firms employ RPA and other emerging automation technologies, while 50% will prioritize it in 2022 and beyond.

Besides overburdening workers, companies still have to learn to manage the volumes of continuous risk data, and use it to avoid disruption while increasing efficacy and improving resilience.

Automating the risk management process ensures businesses:

Monitor risk efficiently, cost-effectively, and at scale
Collect valuable data for sentiment and impact analysis
Improve accuracy, detection, confirmation, and prediction of risks
Have greater visibility into their actual risk exposure
Free human resources to focus on the highest risk mitigation actions
Distribute risk actions to be undertaken to the right human workers

Banks, for example, can automate their credit risk management processes to protect themselves from credit risk threats like counterfeit documents.

Automation technologies like RPA, AI, and ML analyze customer applications to determine an applicant's risk. Within minutes, these tools flag fraud or credit risk threats—not just for one but volumes of applications.

Inscribe's fraud detection and automation platform helps financial services providers determine the authenticity of applicants' documents—like bank statements or utility bills. This minimizes their credit risk and saves them time and money.

Risk management automation use cases

Organizations today face higher and more challenging regulatory requirements or expectations, increasing costs, and demand for transparency, leading them to prioritize technology.

Companies automating risk management and regulatory compliance processes apply technology to monitor, control, and report on business risks quickly, flexibly, and cost-effectively.

Risk management automation streamlines and improves overall efficiency in multiple use cases, including:

Investigating suspicious activity alerts: Resolving alerts involves judgment-based, standardized, and repetitive tasks ideal for automation. Software bots accelerate issue resolution while improving fraud detection and management.
Onboarding customers: Bots collect, collate, and retrieve customer data from disparate systems and external sources (e.g., regulatory and law enforcement agencies) for faster customer onboarding.
Compliance reporting: Human workers don't need to waste time and effort building compliance reports. Instead, software robots can automate email messages, deliver them to the relevant parties, and free up employees to do higher value-added work.
Internal management and external regulatory reporting: Business teams collect and assimilate data manually from various sources. Automation eliminates the redundancy in this process, delivering accurate and quality reports for better analysis and review.
Limit management: Risk officers manually collect, assimilate, and analyze data sets when reviewing counterparty exposure limit breaches. Automation makes the approval or rejection process faster, more accurate, less time-consuming, and more efficient.
Reconciliation: Managers need information in real time to make timely and fast resolution of issues. Automation performs business rule checks, turning in results faster, discovering errors and anomalies, and providing timely reviews of internal and external reports.
Surveillance: Automation also helps stock trading companies manage risks and compliances required to trade stocks while conducting compliance checks to post trades.
Credit loss computation: Finance service providers usually compute information in financial statements and on expected credit loss. Automation eases credit loss computation for employees, reducing the risk organizations face through this process. Software robots extract data from financial statements to calculate and report the expected credit loss.
Anti-money laundering alert investigation: Automation eliminates the manual work involved in researching and resolving AML alerts. This hastens the process and reduces risks and costs companies may incur.
Regulatory and compliance reporting: Reporting takes time and involves a lot of manual effort. Automation helps enterprises collect data, prepare regular compliance reports on time, and submit accurate information to the relevant regulatory authorities. This avoids infringements or penalties for non-compliance.

How to automate the risk management process

A professional assessed documents as part of a risk management process.

You need a clear vision of the desired goal before incorporating an automated risk intelligence and action plan into your risk management process. To get there, you need to:

Identify risk events: Research, collect, validate, and analyze risk intelligence data to identify events that meet or exceed the preset risk criteria and forward them to the risk intelligence system.
Assess: Assess the level of risk exposure to determine the right risk mitigation actions for each risk event.
Control: Assign risk mitigation actions that require human judgment for the most critical risk events.
Monitor and report: Ensure the risk management automation model performs well in appropriateness, relevance, and accuracy in each use case. Check on the technical performance, legal and regulatory developments affecting the data, and operational and business outcomes it achieves.

Mitigate risk through automation

To survive in the new era of cascading risk, organizations need to identify potential risk events early to mitigate them to prevent business disruptions.

A risk management strategy that includes automation technologies detects cascading disruptions in your business processes, among third parties, and more.

Rather than waste hours on manual, repetitive tasks, free your team to work on more interesting tasks requiring human intelligence. This way, they can focus on keeping customers and business partners satisfied.

Ready to automate your risk management process?