How RiskNest Automates Cyber Risk for Banking and Financial Services?

Cybersecurity has become an increasingly important area of focus for banking and financial services organizations, as cyber-attacks can have severe financial and reputational implications. As per an article from McKinsey and Company, the sooner banks start their journey and establish an effective approach to model risk management of cybersecurity solutions, the quicker they will be able to manage risk and establish controls. Understanding cyber risks in financial terms has become a critical aspect of the overall risk management strategy for banking and financial services organizations.

The article "Insights from Cyber Coordination Groups 2020" provides a comprehensive analysis of the effectiveness of cyber coordination groups in the UK. The group represents sub-sectors – Insurance, Investment Management, Fund Management, Retail Banking and Payments Firms, Retail Investments and Lending, Brokers/Principal Trading Firms and Trading Venues/Benchmark Administration Firms. Members of the Cyber Coordination Groups (CCGs) identified several key areas of risk and emerging trends in cybersecurity. 

Unlock solutions to the challenges faced by banks and financial services organizations in their cyber risk management and quantification journey.

Download White paper

Challenges in current approaches to cyber risk management for financial services organizations

The current market for cyber risk management solutions offers a dizzying array of options that generally fall into two categories: qualitative ranking and manually created quantification reports. While these approaches may be useful to understand the basics of cyber security risks, they lack the necessary granularity and specific financial information needed for banking and financial service organizations to make effective capital allocation decisions.

Qualitative ranking approaches to cyber risk management are based on reporting the security status on a red, amber, and green or rating the security risks based on a predefined scale like 1-10 or 1-100. Although this approach may be useful, it lacks the necessary specificity and granularity required to make effective capital and time allocation decisions. On the other hand, more mature financial services organizations use consulting organizations to build quantification models. This approach is expensive, time-consuming, and heavily reliant on the skills of the consulting organization, leading to a lack of standardization and peer benchmarking.

As per a Deloitte report, even though there is plenty of money available to combat cyber risks, Financial services CISOs say there is never enough time to address everything they are being asked to accomplish. Those interviewed juggle a multitude of responsibilities as they scramble to secure legacy systems and applications, contain a barrage of emerging threats, and establish a more proactive, innovative, and comprehensive cyber risk management strategy across their organizations. The burden can be daunting; one major FSI said his company faces between 5,000 and 6,000 attempted intrusions every day, estimating that about 1 out of every 20 people who access their systems is trying to steal something.

A new approach to cyber risk automation for Banking and Financial Services organizations

To overcome these limitations, Alfahive has developed a cyber risk automation platform tailored to banking and financial services organizations. The platform has an industry-specific, data-driven approach and leverages machine learning models to quantify cyber risks in terms of both financial exposure and the probability of an impact. This is done using OPEN-FAIR cyber risk quantification methodology and Monte-Carlo simulation, with pre-populated data on threats, losses, and control prioritization.

RiskNest^TM also includes a patent-pending, cyber incident susceptibility model, which includes an inside-out and outside-in, comprehensive, susceptibility score. The platform's recommendation engine automates the security control prioritization based on the reduction in risks, designed to maximize the return on the security capital. By focusing on risk reduction, the recommendation engine helps organizations achieve a higher return on their security investments.

The Alfahive platform comes pre-configured with various banking business functions, including retail banking, corporate banking, investment banking, wealth management, back-office operations, risk management, compliance and regulatory, corporate functions, and information technology. These functions can be customized based on the bank's specific business model. 

The Alfahive research team has worked with members of the FS-ISAC community to identify the most important cyber risk scenarios faced by banking and financial services organizations. Using cause-and-effect modeling, the team has developed a set of pre-configured risk scenarios that can be easily adapted to meet the unique needs of individual banks. These scenarios cover a range of risks, including data breaches, ransomware attacks, network outages, denial-of-service attacks, and fraud related to business email compromise and phishing scams. In addition, the scenarios include compliance and regulatory risks as well as third-party risks associated with external parties such as vendors and suppliers. By automating the identification of these risks, the Alfahive platform helps banks better understand and mitigate the risks they face.

Unlock solutions to the challenges faced by banks and financial services organizations in their cyber risk management and quantification journey.

Download White paper

Time and Cost Savings for Risk Managers

Alfahive's platform offers several benefits to organizations, one of which is the recapture of many hours of productivity. By automating assessments and reducing the effort needed by over 50%, the platform eliminates manual and resource-intensive approaches. Moreover, Alfahive's platform has pre-built data specific to the industry and geography, with already trained machine learning models, leading to a 10X faster time to value for risk quantification. This means that the typical risk quantification program that takes approximately 8-12 months can be completed in about 4-8 weeks with minimal training and consulting resources. Additionally, by making data-driven decisions on control improvement prioritization and linking risk operations to security operations, the platform helps organizations gain efficiencies and uncover opportunities to do more with less.

In conclusion, with the increasing threat of cyber-attacks, banking and financial services organizations need to have automated cyber risk management and quantification solutions. Alfahive's RiskNest^TM platform provides a unique, industry-specific, data-driven approach to cyber risk automation, which empowers organizations to make informed decisions to manage their cyber risks effectively. 

‍Schedule a pilot with us today and see the value. We invite forward-looking organizations to engage in a proof of value pilot to demonstrate the time and cost savings our platform can drive for risk managers.