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As we move into the post-pandemic era, the value of cloud computing technology is going to increase. Needless to say, companies would have to be at the forefront leveraging it to gain an edge in this fiercely competitive market. Today, these entities require a secure framework comprising a cloud-based architecture that scales dynamically, adapts fast, withstands failures, and uses real-time data to facilitate their IT and data operations. They also need to align with the latest governance and mandates of regulatory changes.
A company that aims to make a difference by providing cutting-edge solutions to this industry is Finserv AI (FS.AI).
FS.AI provides solutions such as intelligent automation and resilient operations to the industry through innovative mediums like artificial intelligence (AI), machine learning (ML), robotic process automation (RPA). They have built a framework and an open-source toolkit to accelerate digital operations and manage its risks for leading banks, insurance companies, and capital market firms. The company offers in-depth experience in AI and data governance from backgrounds in Big 4 Strategic and Management Consulting, Silicon Valley & Silicon Alley Cloud and Fintech startups, and executive or leadership roles in leading global financial services firms.
In a conversation with CIO Applications Magazine, Art Nazzaro, Principal of Finserv AI, focuses on the pain points of the financial services industry and how FS.AI offers innovative AI/ML solutions and lab environment that facilitate digital transformation and help companies deal with security and compliance issues.
What are the present pain points in the AI Solutions space, and how is FS.AI addressing these issues?
CIOs of the financial services sector is no different from those of other industries. They remain under a certain degree of constructive pressure to introduce transformational products to their businesses and customers. We have noticed a particular emphasis on better security, regulation, compliance, and risk management amongst our clients. They are trying to integrate security operations, recoverability, and continuous integration/continuous delivery (CI/CD) processes into a resilient cloud deployment model to support hybrid distributed architectures and require the operational reliability of full-stack engineering. Toward these ends, we craft resilient solutions that effectively build products that align with transformation risk management protocols. To support de-risking, our clients tend to invest in AI, automation, and model management services.
We are able to assess and control inherent risks; deploy augmented process discovery; and establish workflow automation and greater resilience through the use of AI/ML systems
Our company supports three lines of defense for our clients—IT and business line; operational & security risk management; and internal & external assurance and audits. We offer our clients the opportunity to engage with the stakeholders in an AI resilience lab environment, where we utilize AI/ML toolkits to provide high-quality learning and development and gain greater participation in building cooperative solutions.
In the past year, the COVID-19 pandemic has dramatically impacted our industry and requiring it to become more adaptive, resilient and improve its governance and risk management factors. At Finserv AI, we work with CIOs and CTOs, COOs, and CROs looking to launch transformational products or build improved operational risk management systems. We are able to assess and control inherent risks; deploy augmented process discovery, and establish workflow automation and greater resilience through the use of AI/ML systems.
Cite an example or two about how your services have enabled your clients to overcome their hurdles to attain positive results?
Our most recent client was an insurance company. The insurance industry has been mandated by a regulatory change—IFRS 17 and follow-up IFRS 19—which our client needed to implement within a year. Among the future controls, these accounting mandates have put into place include the improvement of data transparency informing deferred expenses and the accuracy of mark to market data informing the calculated value of investments. We worked closely with controllers and accounting teams alongside the company's CFO to assess the time it takes to close books and records at the end of a quarter—which was approximately six weeks. We addressed processing deferred expenses and accurate investment values in the lab utilizing augmented data discovery, which helped to establish a unified dashboard and drive process automation which also improved time to complete closing. We reduced the 45 days cycle to closer to 30 days. Our objective was not to replace the client's present accounting systems but rather to build new data ingestion and establish an integrated end-to-end process to enable AI/ML algorithms to automate critical workflow, previously mostly manually executed on-premise and manually validated using spreadsheets. This new way of operating produced new, improved accuracy and better-governed outcomes while saving up to 25 percent over the previous cost of financial closing efforts and operations.
Another case included building an integrated governance risk and compliance (GRC) tool for our client to alleviate the process bottlenecks that hindered them from embracing the goals of implementing cloud technology. Here we successfully facilitated communication between IT, the business, and risk management by developing a unified dashboard for a common model to support technology and operational risk management goals. It enabled the company to introduce their IT product in the cloud more efficiently through their continuous integration continuous delivery (CI/CD) pipeline while addressing the transparent risk and controls environment mandated by the company's compliance policy and standards.
What are the distinctive features that give FS.AI its competitive edge?
We have adopted and applied current proven AI methodologies and incorporated them into our AI Scaffolding ™ and AI Resilience Lab ™, which enables us to leverage scalable process and workflow models to build machine learning capabilities. AI Scaffolding refers to the creation of autonomous, semi-autonomous, and supervised systems used to solve current critical process pain points. Our AI Resilience Lab promotes an engagement approach offering a high-quality, transparent development environment to our client's IT, business, and risk stakeholders. Here sustainable operational transformation is realized using synthetic and real data simulations to automate and improve critical business process capability and resilience. Through these methods of active engagement using current digital operational tools, we are purposely changing the way we operate so that our clients can change the way they operate.
What plans do you have for your company's future?
We are focused on building upon the success we have achieved so far. In the near future, our new efforts are emphasizing the implementation of new data virtualization and data ops breakthroughs to support secure, high-performance enterprise operations. Nowadays, data needs to be near real-time, accurate, consistent, secure, event-driven, and immutable. We are engaged in leading open source forums and in building tech partnerships with major technology firms, leading risk product vendors, and top ten cloud providers to be there first in supporting our customers' digital operational transformation.