Risk Analytics

Risk intelligence (RQ) uses forward-looking risk concepts and tools to make better decisions, alleviate threats, capitalize on opportunities and create lasting value for companies. Organizations with high risk intelligence tend to make more informed business and security decisions than those with low RQ. We are at the forefront of helping organizations use advanced analytics for risk management. Organizations want to harness data that is locked in silos and a variety of external sources. We build advanced analytics systems to give organizations clearer visibility into the challenges associated with managing many types of risk in key areas such as operations, regulatory compliance, supply chain, financial services, e-commerce and credit.

As experts in developing big data-related solutions, we are able to combine new sources of unstructured information with state of the art advanced modeling techniques and tools to provide insights quickly. We have a passion for helping clients produce models with significantly higher predictive power. Our approach to building risk analytics is pragmatic and helps companies establish a baseline for measuring risk throughout the organization. This offers executives clarity in identifying, viewing, understanding, and managing risk. The illustration below shows different areas of our advanced analytical capabilities in the area of risk management.

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Credit Risk

Credit risk is a major liability to organizations if not assessed and monitored with due diligence. It becomes a critical consideration for organizations to effectively assess credit risk. At DataFactZ we have developed advanced models to address major factors and understand customer behavior through the vast amounts of available data. We assess their risk appetite, and with increasing penetration of the customer base, we monitor credit risk within the portfolio.

We have expertise in developing credit risk models in the context of the recent Basel II and Basel III guidelines that determine probability of default (PD), loss given default (LGD), exposure at default (EAD) and low-default portfolios. We also use new and advanced techniques for improved credit risk modeling.

Through our credit risk services, we help organizations:

Design and develop credit risk models in the context of the recent Basel II and Basel III guidelines
Develop and validate models such as corporate rating models and retail scoring models
Determine probability of default (PD), loss given default (LGD), exposure at default (EAD) and low default portfolios

Market Risk

Effective market risk management strategies enable organizations to make informed decisions. At DataFactZ, we employ complete, end-to-end advanced analytics to help organizations identify and manage the complex risks associated with the development, deployment and maintenance of complex models used for risk management, valuation, and financial and regulatory reporting purposes.

Our market risk management strategies help organizations:

Review risk management structure, policies, compliances, and governance
Develop and validate models such as consumer credit models, credit loss forecasting models, and models used to support Basel II economic capital estimates
Design & build and credit scorecards
Develop mortgage loan valuation models

Fraud Risk

In today’s connected business environment, all organizations are subject to fraud. Large-scale frauds have led to the downfall of entire organizations, massive investment losses, significant legal costs and erosion of confidence in capital markets. As organizations conduct more business online, internet fraud poses more of a threat, as it is unpredictable and usually does not follow a consistent pattern.

Organizations have traditionally enforced detective process controls, preventive measures and internal audits to prevent fraud. With modern advanced analytical techniques currently available to effectively and efficiently detect fraudulent activity, we can build advanced models that can identify anomalies, trends and risk indicators in an organization. Our systems help organizations audit all types of transactions, continuously or in real-time, allowing management and auditing teams to identify and report fraudulent activity more rapidly. Our expertise in fraud risk spans several industries, as shown below.

Operational Risk

Operational risk measures risk associated with the people, processes and systems required to produce an organization's product or service. At DataFactZ, our analytical capabilities to build advanced models and generate insights in the area of operational risk enables senior management to find a right balance between risk and reward, while complying with strict regulatory requirements.

Our operational risk services help organizations:

Gain in-depth insights throughout the enterprise to effectively identify, monitor, and manage risks and controls across multiple processes of business within a comprehensive governance, risk, and compliance (GRC) framework.
Identify and correct potential organizational non compliance and underperformance issues.
Provide a comprehensive store of knowledge to serve as a basis for standardizing governance, risk mitigation and effective control.
Deliver on demand, actionable reporting on the current state of operational risk throughout the organization.

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Retailers routinely find themselves fighting manipulation of their financial statements and point of sale transactions, conspiracy among vendors, shoplifting, refund fraud, and a number of issues involving salaries, wages, and employee theft. Recently, prominent frauds have involved stolen credit card information and fraudulent merchandise returns.
Traditionally, retailers have implemented manual controls to help minimize potential losses from different types of fraud. These manual controls have several limitations, which can be negated by deploying advanced analytics systems.

At DataFactZ we are the forefront of building advanced analytical models for fraud risk in retail. We believe that basic POS analytics can only take you so far. Deploying predictive analytics to better understand the anticipated sales volume of a given product and anticipated sales of products in the secondary marketplace is an effective way to combat fraud. With this strategy, retailers can identify certain product transactions as outliers and alert stores to increase monitoring of suspect sales or transactions.

We employ big data capabilities to combine structured and unstructured data sources and build a comprehensive model to analyze data from daily transactions, purchasing, accounts payable, POS, sales projections, warehouse movements, employee shift records, returns and store-level video and audio recordings. This approach provides a holistic view of fraud risk and effectively prevents fraud-related activity in retail.

Through retail fraud risk management we help companies:

Build a predictive model to monitor the number of returns, create key performance indicators to monitor thresholds and alerts for product returns and identify unusual transactions
Create dashboards and effective visualizations to monitor and analyze loss prevention related key performance indicators
Determine the root cause and provide a holistic view of factors that contribute to fraud loss
Create custom profiling for ongoing fraud detection, and analyze invoices from suppliers and vendors for irregular patterns to build alert mechanisms for unusual trends

Insurance organizations are struggling to develop effective methods for early fraud detection. Fraudulent claims and underwriting processes pose upfront risks to the business, which directly impacts their bottom line. Traditional manual fraud control activities are costly and only look to historical fraud data, which may not be enough to effectively detect and manage fraud.

At DataFactZ, we are able to effectively and efficiently investigate, detect and manage fraud early in the lifecycle to prevent losses through advanced analytical techniques. Our comprehensive and disciplinary approach for insurance fraud detection combines a number of advanced analytical techniques to increase fraud detection rates with accuracy. We build predictive models to handle all critical external data such as public records, foreclosures and criminal records, and integrate them in a model to analyze potential fraud instances for further analysis.

Through insurance fraud risk management we help companies:

Generate pre-determined anomaly detection thresholds
Combine text mining techniques, sentiment analysis, and content categorization in the fraud detection process to build a predictive model
Create statistical methods and conduct pattern analysis and network linkage analysis to uncover large amounts of data to show relationships
Generate alerts depending on the score of a particular network to uncover early patterns in fraud
Rapidly analyze unstructured data to build effective fraud risk detection methods

Today’s businesses rely heavily on technology to keep operations moving quickly. This reliance on technology creates avenues for people to commit fraud and exploit weaknesses in security and controls and interfere with business applications. However, technology can also be effectively leveraged to combat fraud. In the data intensive banking and financial industries, companies store massive volumes of credit processing data and other types of financial data. Using data mining and advanced analytics techniques, banks and financial organizations are better equipped to manage market uncertainty, minimize fraud, and control risk exposure.

At DataFactZ we leverage advanced technology solutions to implement continuous fraud prevention programs to safeguard organizations from the risk of fraud. Our process leverages predictive models that can be directly integrated with real time transaction systems to reduce data latency and identify and prevent fraudulent activity before transactions are complete.

Through financial fraud risk management we help companies:

Enhance existing fraud detection capabilities with predictive scoring integrated with real-time transaction systems to proactively prevent fraud before transactions are complete
Integrate advanced predictive models with transaction systems to gain business benefits such as real-time analysis of large and aggregate cash transactions
Proactively generate anti money laundering (AML) detection to monitoring risk scores for each transaction, and associate them with relevant transaction codes
Provide valuable insight on organization’s existing AML automated frameworks
Compare trading patterns with known good/bad patterns to predict potentially non-compliant scenarios in real-time
Provide real time risk assessment and scoring to predict risk and exposure prior to trade execution, in real-time