Viktor N, Founder
The finance industry has had its fair share of bottlenecks in the past, largely pertaining to the maintenance of on-premise legacy systems. From duplication of work to inefficiencies in financial processes, legacy systems cripple the ability to provide frictionless services to customers. Additionally, the lack of transparency and ineffective collaborations between financial organizations indirectly fuel malpractices and crimes such as money laundering and fraudulence, creating havoc in the financial realm. Such disparities call for better cooperation between financial institutions and the implementation of an interoperable, purpose-driven platform for cross-institutional automation. In light of these circumstances, Cognive—a blockchain specialist—has built a distributed ecosystem powered by artificial intelligence (AI) to address the challenges prevailing in the industry.
Cognive’s unique value proposition lies in understanding the cross-institutional intelligence required for enterprise automation. This expertise assists organizations in harnessing the power of financial intelligence for collaborative efforts with industry peers. “Cross-institutional distributed intelligence automation allows financial organizations to collaborate on joint ventures without sharing confidential information mutually. Our ecosystem ensures that financial data can be stored on-premise without the need to share them over the cloud,” says Victor Nazarov, founder of Cognive.
The company brings a unified ecosystem, built on the distributed ledger technology, with AI as the vital ingredient for obtaining distributed financial automation data.
The distributed ledger technology of the Cognive ecosystem provides the much-needed transparency of data without compromising on the security. The ecosystem features pre-trained data modules that are automatically upgraded from time to time, (correct: detects unknown frauds cases and learns for continuous improvement. It also secures financial process among systems, people, and business operations for internal audits and departmental investigations.
By configuring the degree of cross-institutional intelligence automation at various transactional levels or in accordance with transactional rules, models, and typologies, financial institutions can launch products and services complying with the rapidly changing regulations. This allows organizations to brace themselves against fraudulence or money laundering schemes. When any of these threats are detected, these institutions can immediately deploy the cross-institutional mechanism with other organizations to proactively combat crimes. It empowers them to think and act collectively, and in turn, exponentially enhance their ability to counter suspicious activities.
One of the financial institutions that Cognive is currently working with had a Transaction Monitoring System (TMS) that was generating an overwhelming number of alerts, most of which turned out to be false positives upon investigating. “Studying the feasibility for automation of financial processes, we applied a unified decision intelligence system that analyzed the financial data, segmented customers, classified and mapped the decisions of analysts related to each false positive case, and created a central recommendation system that reduces the number of false positives,” explains Victor Nazarov, founder of Cognive. The collaboration with financial institution also increased the speed of transactions, enhanced the efficiency of financial operations, and improved the overall efficacy of the process.
The results of the project showcase a 60 percent reduction in the number of alerts generated without reducing the number of analyzed transactions, in addition to decreasing the operational workload by 80 percent. The endeavor also discovered previously unknown threats, inclusive of the crimes that recurred consistently. “With each client interaction, we aspire to develop our Cross Institutional Distributed Intelligence service that enables the proper collaboration between organizations as well as between people and AI,” concludes Victor.