Ensure organisational resilience with
risk analytics

Harness the power of analytics to mitigate risks

The last financial crisis and the pandemic have been fuelling greater investment in data science solutions by businesses and the Banking and Financial services (BFSI) sector in particular, with a strong focus on risk management.
Risk can inherently arise from internal and external sources like lower asset yields, risks from an uptick in commercial lending, market risk, credit risk, Basel III non-compliance, stress testing and customer related risk. BFSI businesses specifically tend to be straddled with these kinds of risks.
While the presence of risk management teams with past technology has helped companies manage risk to a fair extent, newer disruptions in a VUCA geo economic world and a stringent regulatory environment are impacting their functioning, their customers, business performance and long-term resilience.
There are numerous ways in which risk analytics allows businesses in risk prone sectors to better process financial data, analyse it with predictive analytics and mitigate risk.

Reduce risk exposure with PredictiVu’s risk analytics

Stockbroker analyzes the financial chart. Online stock exchange on a computer monitor
PredictiVu uses big data for scalability to process the continuous volume, variety and velocity of customer data that gets generated to arrive at a single customer view. Using predictive analytics powered by PredictiVu’s customised AI and ML models, companies can better assess risks associated with individual customers’ financial activities and portfolios.
Banks, asset management firms, hedge funds, insurance and other financial services companies can particularly benefit from risk analytics.

Our approach to risk analytics

Using advanced analytics, PredictiVu can create alerts to track anomalies and outliers in real-time when issues appear for agile action and immediate intervention. PredictiVu uses real-time portfolio monitoring to assess performance across important factors. When performance has to be improved, swift changes to the portfolio can be made. By screening for risky deals, PredictiVu applies machine learning (ML) algorithms to identify high-risk clients and lower charge-off losses. Companies can track real time credit breaches and analyse risk limit breaches by trader, profit centre, and trading desk level.




Financial Risk
With risk analytics, companies can assess the risks they face, how to improve and expand their operational processes that help them function more efficiently, and whether their investments are being made in pertinent areas.

This includes:

  • Delinquency Modelling
  • Credit Worthiness
  • Transaction Fraud
  • Predictive Underwriting
  • Risk-adjusted premia
Operations Risk
Businesses can employ a variety of approaches and technologies to derive insights, and compute plausible scenarios and forecast future events, with risk analytics.

This includes:

  • Delinquency Modelling
  • Credit Worthiness
  • Transaction Fraud
  • Predictive Underwriting
  • Risk-adjusted premia

Benefits of risk analytics

Prevention of repetitive losses: Analytics makes it possible to identify warning signs that could potentially harm companies. Additionally, it enables timely intervention before the risk incidents turn out to be costly, saving the business recurring losses.

Reduce total cost of ownership – With the flexibility of our turnkey capabilities, businesses can save millions by fast tracking their financial risk management.

Reduce operational cost: Big data analytics enables companies to rapidly identify and analyse all potential difficulties in their margins, as well as adjust their pricing policy as needed. They can reduce operating costs by using BI tools.

Improved financial health: Analytics in risk management helps companies save money. Before shipping goods on credit, Business Intelligence (BI) tools can be used to assess all potential credit risks and the outlying chances of customers making full payment.

Fraud prevention: Using ML technologies, big datasets are analysed to reduce the incidence of suspicious activities and prevent future losses.

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