Data Science for Risk Management

Risk Management Strategy

While 57% of senior-level executives rank “compliance and risk” as the top two risk categories that must be managed, only 36% of organizations have a formal risk management plan, and 69% of executives are not even confident about their current risk management policies. 

The global enterprise risk landscape is highly volatile and teeming with new and critical challenges now and then, and one such example is the ongoing COVID-19 pandemic. 

Despite being a significant organizational process, risk management processes and practices across the globe continue to be a challenging business aspect to manage. 

One of the primary reasons behind this is the consistent technological and market evolution and industry disruptions. 

While having a 100% sure and fool-proof risk management strategy is a utopia, getting data-driven insights from historical data and predictive analytics, in short, Data Analytics, can give you the key to a lot of many concerns. 

How to know whether your business is running at maximum efficiency, and what is your exact risk profile? Do you understand your risk profile in its entirety, and is your exposure to business risks, accidents, and disruptions minimum?

Here, we explore how data analytics can help you resolve these and many other doubts by taking all the guesswork out of your risk management practices. 

Let us begin by understanding the term in the modern digital business landscape. 

What Is Risk Management and What Activities Does It Involve?

Gartner defines enterprise risk management (ERM) as the process of identifying, analyzing, and treating the enterprise’s exposure as visualized by the executive management. 

It includes looking at the various exposures, such as frauds, credit, finance, strategic and operational matters. ERM is a top-down strategy to identify, assess, and prepare for the potential dangers, losses, and hazards that might interfere with the operations and objectives of a firm in multiple ways. 

Hence, instead of being a siloed analysis of different processes, departments, or operations, it is a holistic and highly consolidated analysis of all the business units and segments. 

The following visual shows the various activities involved in risk management:

various activities involved in risk management

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  1. Align ERM Processes to Business Goals. Senior management and enterprise leaders work together to establish the organizational and business goals and educate the staff about the importance of ERM.
  2. Identify Enterprise Risks. Compile a list of various risks that can affect the enterprise and impede it from achieving its goals.
  3. Risk Assessment. Risk examination, keeping the likelihood and impact of risk on the organization.
  4. Select Risk Response. Selecting an appropriate risk response based on the impact and risk appetite, such as risk acceptance, avoidance, reduction, transfer, sharing, etc.’
  5. Risk Monitoring. Monitor the ways risks are changing and how your responses are faring against them.
  6. Communicate or Report Risks. You have to communicate the risk events, their results, and steps you took to counter them, to the stakeholders and upper management, via reports. 

Traditional Risk Management vs. Modern Risk Business: The Paradigm Shift

Traditional risk management approaches are highly subjective and are based on individual perceptions. They also tend to be non-optimal in dealing with the emerging risk landscape. 

Today, enterprises generate limitless data, the technology is getting smarter with every passing day, and business dynamics are becoming highly challenging. Hence, having an effective risk management process is a must-have to steer your business towards growth. 

And so is having a data-based approach towards risk management!

As businesses continue to digitize the operations and automate the processes, the risk profiles continue to assume a more critical, diverse, and complicated stance. 

Why? – You might ask.

Well, this is because of many reasons, such as:

  • Having poor or zero understanding of business goals and digitization needs
  • Profuse integrations with third-party software and SaaS applications increase the risk exposure to security vulnerabilities
  • Migration to cloud to facilitate digital transformation and not investing in the right security measures
  • Adopting highly advanced technologies without analyzing the scale of your business 
  • Not updating and improving your risk profiles and risk management strategies for years 

Hence, to survive in this new digital era, businesses should identify the early indicators of risk events and have to act proactively to mitigate those risks before they become disruptions.

Further, it is important to understand your risk portfolio and requirements for advanced technologies to minimize your risk exposure. 

Below, we discuss some key considerations that you must consider while applying data analytics for risk management.

Applying Data Analytics to Risk Management Process: 5 Key Considerations

How to Measure and Quantify Risk?

While there is no exact science or mathematical formula to measure risk, using analytics, you can easily create measurement parameters that can, in turn, help you in establishing and examining the likely risk scenarios. 

Once this is done, understanding the potential impact of any risk becomes easier. Now, you can start planning around it and gradually establish a baseline of data using analytics to measure risk across the entire organization.

Hence, your end goal should be to use data analytics for the entire organization in a holistic manner. 

Understanding “What Is New” in Analytics

One of the most common doubts people have about analytics is that they have been using it for years, so what’s all this hype about? 

While we have been using data analytics in some form for years, there is a world of difference between that tradition and the current version. The modern data analytics platforms are highly sophisticated, and modern risk analytics is more focused on data exploration, statistical clustering, data segmentation, predictive analysis, scenario analysis, event simulation, etc.

So, even if you have been using numbers for risk analysis and management for years now, modern risk analytics leverages various advanced technologies and is undoubtedly more effective and reliable.

Using ERM Solutions With in-Built Analytics

While having an ERM department with an entire team is a huge asset for every business, it comes at a higher cost, and most organizations have a standalone ERM function. 

Hence, it is essential to opt for professional risk analytics services that can tap into the business-wide structured and unstructured data blocks and understand the potential impacts of a wide range of risks.

Further, as risk management gradually becomes more complex, an ERM with in-built risk analytics becomes a huge overhead. 

Using Analytics for Financial Statements and Reporting

Analytics and financial reporting have a lot of natural overlapping and are mutually fulfilling. Analytics can offer data insights into the posting and characteristics of financial journal entries, enabling people to identify inappropriate accounting, inefficient processes, control overrides, etc.

Statistical methods created on the basis of data insights can also help in defining a transaction profile for detecting frauds and limiting the follow-ups resulting from false fraud detections. 

Hence, analytics can also offer a significant boost to the quality assurance and forecast & reporting mechanisms.

How Can Analytics Meet Regulatory Requirements?

Analytics can facilitate highly reliable, robust, and consistent data to the regulators, which, in turn, allows for better risk-adjusted performance and meeting the diverse regulatory demands such as stress testing, systemic risk management, monitoring, etc.

Using analytics for measuring the risk characteristics of each business line and defining common metrics for monitoring enterprise-wide, risk-adjusted performance is yet another way to leverage analytics for risk management.

Next, we discuss various use cases of data analytics in risk management. 

Data Analytics Use Cases in Risk Management

Risk Modeling & Analytics

Data analytics find significant applications in risk modeling, especially in the financial and banking ecosystem. Advanced credit risk analytics empower institutions with smart insights to make better and surer underwriting decisions. It also helps them increase the business revenue and reduce risk costs. 

Implementing data analytics across all the asset classes, entire credit lifecycle, and credit risk models will help you maximize your profits and contain credit risk within the risk portfolio. 

Some other crucial areas where data analytics finds applications in the risk modeling are:

  • Telecom 
  • Banking 
  • Government Agencies
  • Risk portfolio management
  • Increased penetration of customer base

Data analytics also allows businesses to improve aggregate risk level understanding in a range of stress and baseline scenarios.

The following visual shows the various data analytics use cases in risk modeling in the financial ecosystem:

risk modeling in the financial ecosystem

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Stress Testing

Stress testing is another crucial activity that links to many risk management processes requiring coordination across treasury, financial planning, risk, and analysis functions. Financial institutions have to implement more robust stress testing frameworks with the emergence of regulatory stress tests, like SSM, PRA, DFAST, ECB, etc.

Apart from offering flawless integration across the entire system, a stress testing program should also offer data quality management, process & result validation, expected loss modeling, and forecasting & reporting. 

Data science offers an in-depth analysis of enterprise-wide data (historical and current) to help you draw actionable and reliable risk-related insights. It also paves the way for creating a collaborative, repeatable, and auditable stress testing program.

Fraud and Risk Detection

Data sciences allow you to create well-defined customer segments based on personalized parameters, such as current credit, past expenditures, fraud history, financial well-being, etc. So, you are able to segment all your customers into different categories depending on the probability of them causing fraud. 

Creating such segments also allows you to target all the customers in a refined and more definite way via personalized products. You can monitor and track the customers with higher fraud risks well ahead of time and improve your risk profiling.

Take a look at the following image to learn how data sciences can improve your fraud detection and management easier:

data science for fraud and detection as part of risk management strategy.

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Operational Risks

Operational risks focus on the way things are accomplished in an organization and are the risk of incurring losses because of flawed or failed processes, systems, policies, and disruptive events. Data science offers advanced operational risk and compliance analytics, allowing you to protect your capital with more reliable and robust operational processes.

Using smart data insights, you can solve the most complex and critical analytical problems across all your non-financial risk types as well, such as:

  • Loss and scenario-based risk models
  • Stress testing

The government and public sector financial organizations can also leverage data science for managing highly complex risks with diverse problem sets, such as money laundering, unauthorized behaviors, and customer intelligence frauds. 

Model Validation

Risk model validation is an important area of research and involves the activities and processes that verify the model performance is as per the goals and intentions. It is a core element of model risk management, and advanced data analytics capabilities facilitate model validation across all the model classes. 

Such analytics play a crucial role when it comes to measuring quantitative information, such as the market size and value transactions of a firm. A common example of model risk is the failure of a firm to operate adequately or financial crash, etc. 

Having a reliable, robust and well-defined risk model is a business must-have for all enterprises, especially the ones with distributed resources and assets. 

Using data sciences and analytics, the leadership can make smart risk-management topics, such as:

  • Defining model governance procedures and policies
  • Identifying risk model needs
  • Validating risk models
  • Offering necessary organizational capabilities

Institutional-Investment Analytics

Institutional investments, such as large pension plans, endowments, sovereign wealth funds, etc., are done at a large scale and are vulnerable to mountainous risks and frauds. 

Using the advanced analytical capabilities of data science, institutional investors can understand the risk and return potential of their risk portfolios in a better manner. 

Some of the core insights include:

  • Risk factor analysis
  • Liability and model asset matching
  • Asset-allocation methodology enhancement

In association with Machine Learning and Big Data, data analytics can also offer crucial insights such as risk-adjusted returns by disaggregating the sources of investment costs and performance.

Predictive Risk Intelligence

Predictive risk intelligence or risk monitoring involves using current and historical information to identify emerging and potential risks. Using ML and AI-empowered data analytics, you can discover the hidden trends from the organizational risk data and get crucial insights about the future emergence of different types of risks. 

With advanced notice of emerging risks, potential loss, and exposure to different types of risks in the future, you can make smart decisions well ahead of time. 

Some of the best advantages of using data science for predictive risk intelligence include the following:

  • Increased operational efficiency and resiliency
  • Improved cost-effectiveness
  • Optimized return on capital
  • Improved stakeholder confidence
  • Time-to-market acceleration

Insurance Analytics

Insurance is one of the highly competitive industries with very limited predictability. Further, it is highly prone to risk and is dependent on statistics. However, data sciences offer a wide range of information sources for risk assessment and reduce this dependency on statistics.

Advanced analytics and predictive analysis can help identify risks, improve the claim assessment methodologies, and offer targeted insurance policies. Further, they can offer fraud detection, price optimization, personalized marketing, and customer segmentation.

Using predictive analytics in insurance will enable insurers to identify and target the most profitable potential markets. Data can uncover hidden trends, behavior patterns, common demographics, and characteristics, which can help them to target their marketing efforts.

Some other notable use cases of data sciences in the insurance industry are:

  • Lifetime value prediction
  • Recommendation engines based on special algorithms designed for data analysis
  • Healthcare insurance
  • Risk assessment 
  • Claim prediction

Cognitive Risk Sensing

Risk sensing refers to “sensing” the upcoming risks in the time ahead via the cognitive abilities of advanced data sciences, such as AI-based analytics. 

Cognitive risk sensing synthesizes crucial data globally to offer crucial insights such as:

  • Early detection of various issues
  • Proactive monitoring of emerging events
  • Driving business growth and mitigating the risk impacts
  • Keeping the main focus on decision making instead of data analysis

Using advanced data sciences’ cognitive risk sensing abilities also empowers businesses by offering smart alerts about threats and opportunities. So, you can enhance the quality of your data insights and understand the potential impacts of business policies on your organization.

Analytics-Based Valuation

Recent studies reveal that 24% of the investors have changed their valuation opinions because of data analytics, and 38% of them would likely change their valuation opinions in the coming two years. 

Analytics-based business valuation is fair, precise and immune to manual business valuation errors. In association with other advanced technologies, data analytics can make valuation activities and financial disclosures supportable, efficient, fair, and more transparent. 

Further, data sciences can help businesses forecast business performance faster and develop and test brand offerings at a fast pace. Businesses can segment their customers and tailor their offerings on the basis of a thorough analysis of their behavior. 

Some other use cases of data sciences in analytics-based valuation are:

  • Improve operating performance via cost-reduction, inventory shrinking, and optimal resource allocation
  • Use algorithms for smarter decision making
  • Improve organizational profitability
  • Reduce supply chain risks
  • Expose fraud and regulate business practices

Analytics for Business Risk Assessment – Looking Beyond the Tip of the Iceberg

Just like an iceberg, the risk portfolio of a business organization is massive and hidden. Data science is the only reliable tool that not only helps you make sense of loads of unstructured historical data but also prepares you for the upcoming risks and events that can bring your business down to its knees. 

However, it is important to leverage the analytics and technologies in the right manner to get the best results.

Hence, we recommend investing in highly advanced, smart, and intuitive data science-based solutions for risk management, which are tailored to your business-unique requirements. From choosing the right use cases to choosing the right technology, such platforms help businesses gain predictive intelligence as well as gather actionable insights from the historical enterprise data.

So, take the first step towards better and smarter risk management by choosing the right data science platform today!

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Kavika Roy

Kavika Roy

Kavika is Head of Information Management at DataToBiz. She is responsible for the identification, acquisition, distribution & organisation of technical oversight. Her strong attention to detail lets her deliver precise information regarding functional aspects to the right audience.