How to use digital tools and AI to improve risk management

From geopolitical instability to climate change and evolving regulations, risk managers are spinning more plates than ever before.

Many are now turning to artificial intelligence (AI) and digital tools to automate manual processes, improve decision-making, and enhance risk mitigation strategies.

The problem: Gathering risk insights

“One of the key challenges that risk managers face today is accessing timely data,” said Joseph Chew, account director at risk software provider Camms, during a Parima webinar.

He outlined a common risk management scenario — the risk manager is preparing their risk report but realises that 40 to 50% of the business units have not updated their risk records.

“Then you send out emails and follow-ups, have phone calls, urging each of these departments to do the updates because you have practically left less than about a week to prepare the report,” he continued.

“Risk managers should be analysing risk data, advising the board, implementing controls, and making operational changes to reduce risk. Instead, they are focusing on admin tasks”

“This is not a one-off. This really could be a quarterly routine where you have to do a lot of follow-ups. You get worried whether you can submit your report on time and if you can submit a good report for your board.”

Many risk managers still using programmes such as Microsoft Excel to manage risk registers, which does not help with the workload and leaves less time to do more strategic risk operations.

On top of that, such processes lead to costly and time consuming errors, including broken formulas, cut-and-paste errors, version control issues, and limited scalability for growing datasets.

Chew said: “Why do organisations employ risk managers? Risk managers should be analysing risk data, advising the board, implementing controls, and making operational changes to reduce risk. Instead, they are focusing on admin tasks, such as chasing outstanding reports.”

How digitisation and technology innovation can help

Digital solutions can help to overcome some of these challenges, helping risk managers to eliminate manual processes and focus on value-adding activities.

Benefits of this approach can include:

• Real-time data updates
• Single source of information
• Eliminates version control issues
• No scalability constraints
• Enhanced data accuracy

For instance, automation can be used to gather risk data and send reminders to various departments to ensure they update their risk registers.

There are three key areas to focus on — AI, visualisation, and advanced reporting.

• Artificial intelligence: machine learning, natural language processing, predictive analytics, and process automation.
• Visualisation: bowtie analysis, Monte Carlo simulations, and heat maps, and then present these to the board
• Advanced reporting: chiefly beneficial as an ingestion source for multiple data sources.

Chew said: “Machine learning [can] access tons of data and discover new information. For example, certain trends or regional controls that enable you to understand cultural differences.”

“Natural language processing can read a lot of documents. For example, on third-party risk assessments, you can get inputs from your audit reports, your assessment reports, or your test reports from your vendors. AI can get all that and do the reporting for you. You do not even need to generate reports; they can just flow to your email.”

How to get the most out of digitalisation

To maximise AI’s benefits, risk managers should work closely with internal teams to ensure smooth implementation.

Automation can also reduce inefficiencies in internal communication and enhance how risk professionals engage with leadership.

“We always need to think about how we communicate with senior management. If the information is presented in a way that is easy for them to understand, it is much easier to get buy-in,” Chew said. This means using AI-driven dashboards and visualisations to present risk data in a more accessible format.

“Some companies have started using AI for predictive analytics, which allows them to identify potential risks before they become serious issues”

Beyond internal collaboration, Chew advised risk professionals to connect with peers who have experience using AI in risk management. “Reach out to experienced risk managers who have experience running projects, running all this software itself that they are all ready to share with you,” he said.

Organisations already using AI successfully can serve as useful reference points. “Some companies have started using AI for predictive analytics, which allows them to identify potential risks before they become serious issues,” Chew said. Learning from these examples can help risk managers decide which tools and strategies to adopt.

Chew’s final message was that AI and automation are not futuristic tools — they are practical resources available today.

Risk managers should start by exploring relevant AI applications rather than focusing on theoretical knowledge. “Really gain a better insight before you plan out for your next stage itself,” he concluded.