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Financial Risk Analytics


Inarguably, the finance ecosystem has always remained the most –sensitive of all the economic sectors in every part of the world. However, over the decade the sudden surge in the number of financial products has not just fast-paced the competition but also led to the growing risks. Thus, to mitigate such financial risks Financial Risk Analytics has been brought to common practice across several Financial Institutions.

What is Financial Risk Analytics?

Financial Risk Analytics is the modular cloud solution addressing the evolving financial risk and regulatory requirements. The measurement and management of all forms of risk such as counterparty credit risk, regulatory risk, market risk, capital risk, etc become easier with the Machine Learning and Big Data stack of Financial Risk Analytics. The scalability and ease of deployment make this data analytics tool a powerful internal resource to help prioritize business goals. The use of the latest technologies and analytics like vectorized pricing libraries has led to its mass adoption across the globe.

Use Cases of Financial Risk Analytics

Financial Data analytics in risk management has extensive impacts and has made it the most used financial tool. Also, the decision relating to credit and financing gets a big boost with the emergence of this profound technology. Thus, the extended use cases of financial risk analytics are as follows:

Use CaseDescription
Risk Analysis and ManagementThe creation of a scoring model and cost optimization techniques based on financial raw data is possible with Financial Risk Analytics. The data analyst and scientists use the Machine Learning algorithm, fundamental and technical analysis model to analyze the creditworthiness of the customers and help understand their loan repayment capabilities.
Customer Data ManagementData forms the core of every research and analysis process be it structured or unstructured data.  Customers are the prime focus of every Financial entity therefore it is necessary to use effortless data acquisition and analysis processes to predict customer behavior. With the help of Business Intelligence which is the extended concept of Machine Learning and Natural Language Processing customer data mining and risk management processes become seamless.
Fraud DetectionFraud can be explained as the calculative risk which generally is a result of ignorance. Thus, to predict fraud and take requisite measures is a goal that can certainly be achieved with the help of Financial Data Analytics. One of the most common risks observed in the financial ecosystem is Credit Card risk or scam which can be avoided with the right Risk Data Analytics practice in use.
Personalized Services In a world where every person is accustomed to distinct needs and beliefs, no one product or plan can serve the purpose for all. Similarly, to address the personalized expectations of the customers and deliver the right financial framework, it is essential to power the analytics techniques and studies the probability of risk through Financial Risk Analytics.
Algorithmic TradingTrading is the newest financial trend where the application of Data Analytics and Data Science concepts has brought a major revolution. Even the massive amount of data facilitating algorithmic trading can be processed effectively with profound analytical engines. Hence, devising the new trading strategies has ensured smoothness in the Financial Institutions.

Metrics used for Financial Risk Analytics

There are several tools used by individuals, governments, businesses, and institutions to calculate the amount of financial risk in a stipulated period. Some of the most commonly used Financial Risk Analytics tools are as follows.

Fundamental Analysis

It is the process of measuring the intrinsic value of the securities by analyzing all the underlying aspects of the business ecosystem.  Here, the nature, value, earnings, and risks associated with the financial products are determined.

Quantitative Analysis

  • The analysis theory evaluates the historical data and calculates the financial ratio to define the risk attached with the assets, securities, or borrowers. Some of the common financial ratios used for the purpose are :
    • Working Capital Ratio
    • Liquidity Ratio
    • Debt-to-Equity Ratio
    • Capital Expenditure Ratio
    • Financial Assets Ratio

Technical Analysis

The tool focuses on studying the historical quotes of the financial products and evaluating their future value. With the help of technical analysis, the respective institution gets deeper insight into the financial market and their related ecosystem giving the right picture of the Financial threats.

Qualitative Analysis

  • The study of Financial Risk requires effective collaboration between all the Risk Management resources available for the project. The popular qualitative Financial Risk Analytics techniques are:
    • Delphi Technique
    • Decision Tree Analysis
    • SWIFT Analysis
    • Consequence/ Probability Matrix
    • Bow-tie Analysis

Examples of Financial Risk Analytics

The most talked-about Financial Risk Analytics has some practical applications where precision talks louder than probability. As a result, financial institutions and professionals across the world have started making the best use of Artificial and Business Intelligence in managing risk.

Stress TestingThis software testing activity that helps to ensure the stability of the provided system is where Financial Risk Analytics have proved their profound importance. Since the process involves testing beyond operational activities therefore risk analysis becomes an obvious task for the Financial Services entities.
Credit Risk – CCR and CECLBeing quite prevalent in the modern world, Credit Risk comes with the need to address at the earliest. Thus, Financial Risk Analytics has proved useful in determining the Counterparty Credit Risk (CCR) and Current Expected Credit Losses (CECL) in companies like Accenture.
Liquidity and FundingBefore financing the customer, every financial institution conducts a thorough data analysis of their financial statements to understand their creditworthiness. The inclusion of Machine Learning and Deep Learning algorithms in the risk analysis process helps to take precise funding decisions. Additionally, also helps to predict the liquidity status of the client and analyze if the client would be able to meet repayment obligations.
Collections and RecoveryIn some cases, the default in making repayment is the avoidable risk whereas in the other cases where loss comes along, the collection and recovery become the priority task. Thus, Financial Risk Analytics helps to predict such losses and unavoidable risks so that the collection process can be put in place at the earliest.

Popular Financial Risk Analytics Software

Information collected and analyzed is used for decision-making or strategy formulations practice. Similarly, to support the Financial Risk Analysis practice the experts have developed software to ensure easy report creation and presentation. Some of the popularly used financial analysis software includes:

Oracle Financial AnalyticsIt is the modular component of Oracle with the major specification of Business Intelligence Applications. The software helps to provide deep insight into the general ledger and facilitates performance tracking, customer satisfaction, and revenue flow.
SAS Business AnalyticsIt is the integrated environment for data mining, predictive modeling, text simulation where the relationship between the historical, present, and future data can be established. Thus, it helps to generate a report on customer preferences.
IBM Cognos FinanceIt is considered the most effective financial tool with highly proficient data analysis capabilities for not just risk management, but also supply chain procurement and workforce management.
SAP ERP Financial AnalyticsThe software was developed to help organizations define financial goals by monitoring the revenue and cost standards. The risk analysis is possible with this Financial Analytics by evaluating the present and future value while disbursing the credit and setting the limit.
NetsuiteIt is the financial dashboard with reporting and analysis as their basic function which helps to identify the key performance indicators and ensure effective risk monitoring in the real-time scenario.

A Career in Financial Risk Analytics

The risk in the Financial World is something that can neither be avoided nor delayed. As a result, risk management and analysis are becoming the crucial task which certainly arises the need for professional Financial Risk Analysts too. The Financial Risk Analysts play a crucial role in channelizing the possibilities into opportunities and reducing the scope of risks. In many cases, the Financial Risk Analysts may be assigned the role to deal with direct customers and business houses to evaluate their creditworthiness by collecting data and conducting credit evaluation.

Alongside, such analysts also assess financial statements, reports, and histories for recommending best-suited solutions, alternatives, and credit limits. Thus, the key task of the Financial Risk Analysts is to make decisions regarding loan disbursement and duration for the same.

According to the reports of Occupational Outlook 2020, most of the entry-level credit analysts require Bachelor’s degree in the USA along with specialization in Financial Risk Management.  Moreover, the job market analysis of the BLS states that the United States is expected to witness a growth of 6% in the upcoming decade.

Final Words

Financial Risk Analytics is the practical solution to Risk Management queries worldwide. With the inclusion of advanced technology in the financial ecosystem the decision–making process has grown in efficiency and reduced in time consumption. Moreover, its growing adoption rate indicates the inclination of the world towards relatively less financial risk making the credit environment significantly powerful.

Topics in Data Analytics

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