Data Analytics

With our data analytics advisory services, we pursuit to provide you with information extracted from your raw data by using advanced statistical data models and software.

We provide four different types of data analytics:

Descriptive

Descriptive data analytics is a type of data analytics that focuses on summarizing, describing, and providing an understanding of historical data. The primary goal of this approach is to present a clear and easily interpretable overview of the data to help identify patterns, trends, and relationships within the dataset. Descriptive data analytics is widely used in various industries and fields, including business, finance, healthcare, sports, and social sciences. It provides a foundation for further analysis, such as diagnostic, predictive, and prescriptive analytics.

Real world examples examples are:

Finance

A financial analyst examines historical stock prices and calculates metrics such as average returns, volatility, and correlations with other assets. By visualizing these metrics through line charts or scatter plots, the analyst can identify trends, risk factors, and potential investment opportunities.

Healthcare

A hospital analyzes patient data to determine the average length of stay, the most common reasons for admission, and the average age of patients. By creating pie charts or frequency tables for different diagnoses, hospital administrators can allocate resources more effectively, prioritize staff training, and improve patient care.

Retail

A store manager collects sales data for the past year and creates visualizations such as bar charts to compare the sales of different products or line charts to show sales trends over time. The manager also calculates the average daily sales and the standard deviation to understand the variability in sales. This analysis helps in identifying the best-selling products, seasonal trends, and guiding inventory management decisions.

Diagnostic

Diagnostic data analytics is a type of data analytics that focuses on identifying the causes of past events or issues by analyzing historical data. It goes beyond descriptive analytics, which only provides an understanding of what happened, by delving into the reasons behind those events. Diagnostic analytics helps organizations understand why certain outcomes occurred, identify patterns and trends, and ultimately make more informed decisions to improve their processes or strategies.

Typical examples are:

Finance

A financial institution might use diagnostic analytics to understand the reasons behind an increase in loan defaults. By analyzing loan application data, repayment history, and macroeconomic indicators, the institution can identify factors such as borrowers' credit scores, debt-to-income ratios, or economic downturns that contribute to loan defaults. This information can help the institution adjust its lending policies and develop targeted support programs for at-risk borrowers.

Retail

A retail store might use diagnostic analytics to understand the reasons behind a decline in sales for a particular product category. By analyzing sales data alongside factors such as pricing, promotions, inventory levels, and customer demographics, the retailer can identify the main drivers of the sales decline and adjust their strategies accordingly.

Healthcare

In a hospital, diagnostic analytics can be used to investigate the causes of high patient admission rates. By analyzing patient records, medical professionals can identify factors such as specific treatment protocols, patient demographics, or underlying health conditions that might contribute to admissions. This information can then be used to develop targeted interventions to reduce admission rates.

Government

In the governmental context, diagnostic data analytics can be used to analyze the factors contributing to high unemployment rates and develop targeted policies to address the issue.

Predictive

Predictive analytics is a type of advanced analytics that focuses on recommending actions to optimize a particular outcome. It combines data, business rules, and mathematical formulae such as Markov Chain or Merton model to provide suggestions on how to achieve the best results in the future. Predictive analytics can have a 95% prediction accuracy given the right amount of data.

Typical Examples are:

Finance & Regulatory Reporting

Probability of Default (PD) calculation of loan portfolios of financial institutions

Retail

Optimization of pricing, promotions, and inventory management, considering factors such as consumer preferences, competitor actions, and market trends to maximize revenue and profitability.

Energy Management

Analytics to balance energy supply and demand, optimize grid operations, and manage energy resources more efficiently by predicting equipment failures, scheduling maintenance, and recommending optimal energy production and consumption strategies.

Prescriptive

Prescriptive analytics is a type of data analysis that focuses on determining the best course of action to take in a given situation. It uses a combination of statistical models, machine learning algorithms, and optimization techniques to analyze data, generate insights, and recommend specific actions.

Unlike descriptive analytics, which seeks to understand what happened in the past, or predictive analytics, which forecasts what might happen in the future, prescriptive analytics goes beyond that and provides actionable recommendations that can be implemented to improve business performance and achieve specific goals.

Typical Examples

Energy Management

Run simulations on product/ service mix combinations to max profit.

Finance

Analyze borrower data, economic indicators, and other relevant data to recommend specific actions, such as adjusting interest rates, changing credit limits, or restructuring loans to improve the performing of your loan portfolio.

Our Clients and stakeholders:

Korpodeko relies on RISC's IFRS Calculation Engine (ICE) to determine our Expected Credit Losses (ECL) as well as manage our credit risk portfolio. Next to that, we can always count on their banking, risk management and BI knowledge in case we need support.

Korpodeko

Gerald Stacie

Financial Director

RISC provided MCB with valuable advice, guidance and support with the Implementation of IFRS 9 in 2017 -2018. We still rely on their expertise and business intelligence skills for analysis and preparation of our annual reports.

MCB

Lionel de Cuba

Chief Financial Officer

IFRS 9 brought new challenges to our organisation by shifting our receivable provisioning standards from an incurred - to a forward looking model. RISC showed a great knowledge of the new guidelines and provided unvaluable support throughout the implementation.

MCIS

Hubert Bentura

Finance Manager

At Vidanova Bank, we rely on “RISC’s team of Banking, Risk Management and Business Analysis experts, for advice and support regarding our IFRS9 data modeling, ECL -calculation, -calibration, -analysis and automation process. This provides us with the necessary risk-management oversight and control over our portfolio.

Vida Nova Bank

Nadetta Pablo

Financial Controller/ AMD