Banks often struggle to assess the riskiness of their vast customer portfolios.
Globally, socio-economic volatility is at an all-time high. Even as disruptions continue unabated, banks and financial institutions find themselves in a tight spot when it comes to ensuring business continuity and keeping risk at bay.
Traditional methods of risk assessment use ratings and financial statements and are more of a post-facto analysis. To be able to proactively mitigate risk, banks must efficiently track and assess counterparties’ riskiness on a near real-time basis.
TCS’ Early Warning Framework for Proactive Risk Management facilitates real-time, objective assessment of counterparty risk.
The framework drives lexicon-based search of news articles relating to counterparties and crawls news websites acknowledged and followed by banks. Powered by a machine learning algorithm, TCS’ framework analyzes recently published articles about a counterparty and generates a ‘risk score’ in near real time. If the defined hurdle rate is breached, it automatically triggers mails to identified personnel within the bank, who can then activate mitigation measures.
The framework is built on an open-source stack that allows the configuration of external sources for news capture and runs analytics on them to generate real-time dashboards and alerts.
TCS’ framework can be used for fraud management, know your customer, and liquidity risk, as well as market risk assessment and mitigation.
It helps banks and financial services firms to:
Continuously track a large number of counterparties.
Practice forward-looking risk management.
Offer multilingual support for a comprehensive global view.
Early Warning Framework for Proactive Risk Management
TCS’ framework uses machine learning to drive real-time, objective assessment of the riskiness of counterparties.
The framework scans recently published articles about counterparties and generates risk scores in near real time.