Financial data analytics: applications, benefits & software tools

September 13, 2024

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Financial data analytics

Senior Quantitative Analyst

Financial data analytics is the process of collecting and analyzing raw data from corporate software systems, providing insights into a company’s financial performance. With this knowledge, businesses can make informed decisions, mitigate risks, and improve their financial state.

Itransition helps companies implement robust data analytics solutions to automate financial data management and financial reporting, improve day-to-day decision-making, and build effective financial strategies.

Financial data analytics applications

Companies from data-heavy industries, including financial service providers, use data analytics software to monitor their financial performance, detect risks, predict financial trends, and better understand their customers.

Across industries

Financial performance analytics

Businesses implement financial analytics to track corporate expenses, revenues, and profitability. By analyzing financial statements, they can identify financial risks, spot business growth opportunities, make informed financial planning decisions, and meet regulatory requirements.

Risk analytics

Companies use risk analytics to evaluate the probability of liquidity, credit, investment, and regulatory risks, as well as measure business asset performance under various scenarios. Based on this data, companies can take appropriate risk management actions, prevent financial losses, and achieve greater business resilience.

Financial planning & budgeting

Data analytics help Chief Financial Officers (CFOs) set feasible financial goals and determine the most suitable strategies for achieving them. Relying on financial data consolidation, forecasting, and scenario modeling capabilities, companies can prepare realistic budgets based on the most relevant information as well as swiftly adjust them, quickly responding and adapting to changing market conditions.

In the financial sector

Banking data analytics

Banks use data analytics solutions to consolidate and analyze operational, customer, and product/service portfolio data to target customers with relevant offers, manage credit risks, detect fraud, optimize branch performance, and maintain regulatory compliance.

Investment data analytics

Investment managers and financial advisors leverage AI-powered wealth management software with data analytics capabilities to assess the performance of current holdings against different benchmarks, monitor market dynamics, and test various investment strategies to make informed investment decisions and build well-balanced portfolios.

Stock market analytics

Using stock market analytics, traders monitor stock price fluctuations, perform fundamental and technical analysis, track market sentiment, and forecast financial trends, which helps them make informed buying and selling decisions and set up efficient trading strategies.

Insurance data analytics

Data analytics helps insurers obtain insights into various business aspects, including sales, underwriting, and claim management. These insights enable companies to create granular risk profiles for each customer, accurately price insurance premiums, detect fraudulent claims, and better navigate the complex regulatory landscape.

Customer data analytics

Data analytics enables customer segmentation, customer journey mapping, and customer behavior analysis. By uncovering valuable customer insights, financial service providers can offer relevant products or services to each customer, maximizing customer lifetime value and contributing to revenue growth and profitability.

Drive decision-making with a financial data analytics solution

Key financial metrics to monitor & measure

Despite their size or business niche, companies should monitor a common set of data metrics to accurately assess their financial condition and develop winning strategies.

Cash flow

including operating cash flow and cash conversion cycle, to determine whether a company has enough money to run critical business operations and meet its short- and long-term obligations

Customer profitability

to determine the most and least profitable customers and focus their efforts accordingly or identify cost reduction opportunities

Predictive sales

to help organizations allocate resources more effectively, optimize marketing campaigns, and detect and mitigate sales pipeline risks

Product profitability

to help businesses make more informed pricing, advertising, and product portfolio decisions

Shareholder value

to measure the effectiveness of the current business strategy in generating wealth for shareholders

Value drivers

including revenue growth and profit margins, to assess the company’s ability to achieve its short- and long-term business goals and identify opportunities to improve its financial performance

Essential capabilities of financial data analytics tools

For companies to make data analytics operations the most efficient, we recommend opting for financial data analytics solutions that have the following critical capabilities.

Real-time data processing

The integration, processing, and analysis of real-time data like sales, expenditure, billing, and debt data captured from accounting, ERP, and other corporate systems enable users to immediately get actionable insights. This way, they can base their decisions on the most up-to-date business information, quickly respond to changes or anomalies, and adjust financial plans accordingly.

Data visualization & reporting

Data analytics tools should transform financial data into interactive and intuitive dashboards, making complex insights digestible and actionable. With the visual representations of data, users can easily reveal patterns and trends, better understand the company’s financial performance, and quickly share insights with decision-makers.

Scalability

As your business expands, your financial data analytics solution should be able to handle increasing data volumes as well as various data types and formats without compromising software performance.

Integration potential

A financial data analytics tool that can seamlessly integrate with other corporate systems (like ERP or CRM), software tools (like Microsoft Excel), and external data sources (like banking applications or market data platforms) enables a smooth data flow to the analytics system and ensures the accuracy of the financial data.

Data security

To safeguard sensitive business data from unauthorized access and cyber threats, financial data analytics software has to feature effective security mechanisms, such as data encryption, identity and access management (IAM), and audit trails.

Predictive & prescriptive analytics

Solutions enhanced with predictive and prescriptive analytics help companies accurately forecast future financial trends (including stock prices), customer behavior, and market events, as well as model different financial scenarios. Relying on these forecasts, companies can evaluate the potential impact of business decisions on their finances, plan optimal actions, and optimize business strategies.

AI-powered data recognition

Financial analytics solutions powered by computer vision and optical character recognition technologies can extract structured and unstructured data from financial and other business documents, enabling institutions to use diverse data sources and process large amounts of data.

Benefits of financial data analytics

Adopting financial data analytics solutions brings multiple advantages to businesses both on the departmental and organizational level.

Improved decision-making
Data analytics implementation facilitates data democratization, which results in fact-based tactical and strategic financial decisions.

Operational efficiency & team productivity
Automation of time-consuming data management activities like data extraction, entry, consolidation, cleansing, modeling, and reporting frees employees’ time for high-value activities.

Accurate risk assessment
Companies can assess the risk profiles of their customers in greater detail to identify high-risk customers, calculate financial and service-related charges more accurately, and prevent fraud.

Cost reduction
Based on expenses, procurement, and operational performance insights, businesses can find major cost drivers, draw up spend optimization plans, and identify opportunities for process improvement.

Better financial product design
Financial service providers can analyze customer data across different sources to anticipate the needs of their target audience and shape a more relevant product offering.

Improved compliance
By automating financial data collection and reporting, data analytics software helps reduce the risk of errors, ensuring adherence to regulatory requirements.

Benefits

Challenges of data analytics software implementation

Organizations adopting financial data analytics solutions can face significant barriers, holding back successful implementation and use of the technology. Here are some steps companies can take to overcome the challenges.

Challenge

Recommendation

Data quality & integrity

Inaccurate, incomplete, or inconsistent financial data pulled from different systems can result in unreliable insights leading to erroneous decisions and missed business opportunities.

Since financial analytics software pulls data from various departments, companies should establish a solid data governance framework to ensure the accuracy and consistency of the collected information. As part of such a framework, companies should set out data quality standards as well as procedures for controlling data use and storage, identifying and mitigating financial data privacy risks, and regular auditing.

Employee resistance to change

Corporate finance teams can be reluctant to give up familiar business tools and traditional workflows.

Before adopting a financial data analytics solution, leaders can conduct a change readiness assessment to identify barriers to the transformation and plan appropriate actions to address them.

To boost user adoption, companies should prioritize user-friendly and intuitive solutions that offer self-service capabilities.
Product managers can hold demo sessions to show the team how financial data analytics helps save time.
After the implementation, the company should provide proper onboarding training to teach employees how to use data analytics tools both for their day-to-day activities and more advanced tasks.
Сompanies should create two-way communication channels so that users can quickly notify the IT team of any issues.
To help users quickly resolve issues, companies should set up user activity and log monitoring processes.

These measures will help foster a better understanding of the technology, ensure a smooth software implementation process, and minimize the negative effect of the transition to new workflows.

How we can help

Our team can help you implement a financial data analytics solution or modernize your existing system in line with your current needs, taking care of each step of the data analytics lifecycle.

Data audit

We determine suitable corporate and external data sources and then evaluate your data sets in terms of quality, sensitivity, and availability, outlining the necessary data management activities.

Data integration & storage

We configure ETL pipelines to extract raw data from different sources and clean and transform it into the required format. We also configure scalable data warehouses and other storage options like data lakes, lakehouses, and operational data stores for storing your structured and unstructured financial data.

Data modeling

We build conceptual and logical data models that help you effectively store data and perform the required types of financial data analysis.

Data visualization

We enable interactive data visualization and reporting capabilities within your analytics solution to facilitate quick data interpretation and storytelling.

Build a reliable data analytics solution with Itransition

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