Regulation tech for financial risk planning

Financial entities have to be far more agile while assessing risks. The use of emerging technologies can help them anticipate a wider range of risks in a shorter time

The fresh crisis of confidence in the global banking system brings into focus the importance of compliance and regulation. Regulation technology (RegTech) is becoming increasingly important for compliance and risk management. Regulators, on their part, are also adopting technology to keep up with the fast pace of digital financial transactions. 
 

“We’ve seen real growth in this regulatory-tech and risk-tech space,” says Mark Cooke of McKinsey’s Innovation Data Platform. But while the technology has improved, adoption is lagging. “While we’ve seen businesses build out and digitalise, we haven’t seen the same thing within the risk and compliance community,” Cooke says in a McKinsey podcast. 
 

This is especially important for the banking and financial industry. Many of its constituents have legacy systems, and a transition to digital platforms has not kept pace with technological developments. Regulators in various countries are recommending the use of technology for a range of activities. These include regulatory reporting, transaction monitoring, due diligence and risk management. 
 

Singapore, for instance, is actively encouraging start-ups and innovation companies in RegTech by offering grants. The Monetary Authority of Singapore offers these grants to support financial institutions to enhance their risk management and compliance functions through the use of technological solutions. “The grant also supports upskilling efforts where employees in financial institutions based in Singapore acquire new domain expertise in the use of technology to augment and sustain Risk Management and Compliance operations,” says the Authority. 
 

Risk anticipation and management has contemporary relevance in light of the recent failure of banks in the US. Experts believe that a combination of alert management and smart risk management can predict scenarios in time to take preventive action. 

“The adoption of RegTech cannot occur without further rethinking of the regulatory environment,” says a report by the World Economic Forum. “A probabilistic, performance-based approach — entertaining the evolution of new economic or social scenarios, new technologies and new business models — is required to future-proof regulation and the technologies deployed.”

The report offers the example of the automobile sector. Vehicle makers have to confront a wide variety of risks in their daily operations — from natural disasters and cyberattacks, to supply chain disruptions and changes to regulations. General Motors recognised that its existing systems and processes were not integrated and therefore not optimising risk mitigation. The company worked with IBM’s platform Watson and OpenPages to consolidate their risk management processes and procedures. This helped improve and sharpen its ability to anticipate disruptions.  
 

Manufacturing and services sectors both will increasingly have to adopt a tech-led approach to risk anticipation and management while following regulatory compliance. This demand has also created the need for regulation as a service business model. 
 

“Banks manage risk practices in-house, which requires significant financial and technological expertise. Outsourcing a bank’s regulatory management through RaaS is now a potential avenue and can be an alternative for those who want to quickly benefit without large investments in talent and tools,” says a paper by Infosys Knowledge Institute. 

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Digitisation through the Cloud and AI can better integrate credit risk management practices and enhance risk coverage. Application programming interfaces help ingest data from structured (like transaction and payment history) and unstructured sources such as social media activity and mobile phone usage, the paper says. 
 

Financial institutions have to be far more agile while assessing risks. The use of emerging technologies like machine learning can help them anticipate a wider range of risks within shorter time cycles. With most financial transactions leaving a digital trail, regulators are also becoming better at monitoring unusual and fraudulent behaviour using automated tracking.