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Nov 2 2023

How to Eliminate Barriers to Adopting Advanced Financial Analytics

Financial analytics is the process of collecting, analyzing, and interpreting financial data to gain insights and make informed decisions regarding an organization’s financial performance and strategy. Advanced financial analytics uses more sophisticated techniques, algorithms, and tools to extract insights, recognize patterns and make predictions from large data sets. Using advanced financial analytics, organizations can gain deeper and more actionable insights that help them uncover potential risks and predict and improve performance.

Barriers to Adopting Advanced Financial Analytics

Unfortunately, there are many barriers to advanced financial analytics that a business may encounter. Here are some of the common ones alongside a brief recommendation for how to overcome them.

Over-Reliance on Spreadsheets

Practically all businesses use spreadsheets to handle some aspects of their data analytics. However, spreadsheets don’t offer the integration, scale, real-time data, and advanced analytics required to realize the full potential of your financial data. For these capabilities, companies will need to supplement spreadsheets with a data platform and the right financial tools and techniques to meet specific financial analysis objectives.

Data Silos

Data availability is key to advanced financial analytics. It requires access to comprehensive data, both current and historical. This may include financial records in financial management software, sales data in CRM systems, external market data and economic indicators, news feeds, social media data, and more. To be effective, organizations will need to break down these silos, bringing together data to develop mission-critical insights.

Data Quality Issues

Business users who rely on advanced analytics to make important decisions need to know that they can trust the integrity of its results. While data quality challenges are prevalent across all types of business data, financial data is particularly prone to issues. This is due to manual data entry, complexities when dealing with multiple currencies, customers with multiple accounts, intricate financial calculations, and lack of standard data formats, measurements, and naming conventions. These are reasons why data quality tools to ensure that data is accurate, complete, consistent, reliable, and up to date are so important.

The Complexity of Advanced Analytics

Advanced financial analytics uses techniques such as machine learning and statistical modeling to make accurate forecasts and uncover patterns buried in large volumes of data. Deploying these techniques correctly requires expertise in data modeling and proficiency in programming languages such as Python and R. Even business users will need to shore up their skills in math and how to accurately interpret results.

Strict Regulatory and Compliance Requirements

Financial data is heavily regulated, and violations are expensive. Understanding and enforcing regulations can be challenging, compounded by the need to understand unique requirements across geographies and industries. Businesses will need to establish and enforce policies and processes for collecting, storing, using, and sharing information for advanced analytics.

Communication

Communication can be challenging since various stakeholders throughout a company don’t typically have any background in advanced analytics. Conveying insights and implications of any analysis in a clear and understandable manner is a crucial skill for financial and business analysts to hone.

Sourcing From Multiple Vendors

Data integration, data quality, and other management workloads add more costs and complexity when sourced from multiple vendors. This can limit further investment in financial analytics if its costs exceed the business value it delivers. When possible, look for one data platform that offers many capabilities.

Overcoming Challenges

Overcoming these challenges isn’t always easy, but it’s well worth the effort since advanced analytics can help your business derive valuable insights and make more informed decisions.

Financial analytics success starts with the right platform. Actian has 50 years of experience helping customers manage some of their most critical data. The Actian Data Platform simplifies how you connect, manage, and analyze your financial data. Its unified data management gives you the ability to integrate, transform, orchestrate, and store your data in a single, easy-to-use platform.

Related resources you may find useful:

Data Silos Suck. Here’s How to Break Them Down

5 Common Factors that Reduce Data Quality—and How to Fix Them

How to Maximize Business Value with Real-Time Analytics

Real-Time Data Analytics During Uncertain Times

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How to Use Financial Analytics to Detect Fraud

According to the Association of Certified Fraud Examiners, organizations lose 5% of their revenue to fraud each year. It’s no wonder that financial analytics for fraud detection is in such high demand given this alarming statistic. Fortunately, financial analytics can play a crucial role in helping businesses detect and prevent fraud by analyzing various patterns, discrepancies, and anomalies in financial data and flagging suspicious activities.

The list of use cases for fraud detection leveraging financial analytics seems to be endless, but here’s a breakdown by industry (banking and finance, healthcare, insurance, retail, and telecommunications) of some of the most common examples.

Banking and Finance

Credit Card Fraud: Flags credit card transactions that fall outside the scope of normal activity such as multiple transactions to one card in a short period of time, multiple rush orders to the same address, or an unusually high charge card amount.
Money Laundering: Analyzes transactions and the flow of funds across different accounts to identify suspicious activities such as structuring transactions to avoid reporting thresholds, layering funds through multiple accounts, or using complex transaction networks to obfuscate the source of funds.
Insider Trading: Identifies abnormal trading volumes, unusual price movements, and correlations between trading activities and significant corporate events.
Identity Theft: Flags accounts with unusual behavior such as sudden changes in spending patterns or unexpected transactions in new locations that may indicate that someone is illegally using another person’s data or account.

Healthcare

Fraudulent claims: Identifies claims with excessive or unnecessary procedures, and services that are inconsistent with a patient’s medical history.
Fraudulent billing: Spots unusual coding patterns, phantom billing, upcoding, unbundling, and disproportionate billing compared to peers.
Collusion: Analyzes claims and payment data to detect a high number of patient visits to different providers or patients who may be helping providers charge for tests they do not need.

Insurance

Application Fraud: Spots false information, fictitious beneficiaries, and agents opening and canceling policies to make quotas and bonuses.
Fraudulent Claims: Detects frequent or excessive claims, inflated claims, staged accidents, duplicate claims, and inconsistent information across claims.

Retail

Credit Card Fraud: Flags credit card transactions that fall outside the scope of normal activity, such as changes in the frequency of orders, higher orders than the average use transaction, changes to a shipping address, bulk orders for the same item, and unmatched or suspicious IP addresses.
Refund Fraud: Analyzes data such as the frequency and timing of returns, products returned and their value, and return reasons to discover potential fraud.

Telecommunications

Revenue Reporting Fraud: Examines billing data, contract terms, and revenue streams to identify discrepancies, such as unbilled services, underbilling, or revenue leakage that are likely to be due to fraudulent activities.
Subscriber Fraud: Analyzes subscriber behavior patterns and financial transactions to detect unusual account activities, such as frequent Subscriber Identity Module (SIM) card changes, abnormal roaming behavior, or suspicious calling patterns.

How Actian Can Help

Fraud is increasing both in frequency and amount. With so much at stake, businesses need to either adopt or ramp up their financial analytics to control fraud. Actian can assist you with a new project or help scale your existing analytics deployment. We are a trusted advisor with over 50 years of experience helping customers manage the world’s most critical data.

Actian makes financial data easy. We deliver cloud and on-premises data solutions that simplify how people connect, manage, and analyze data. We transform business by enabling customers to make confident, data-driven decisions that accelerate their organization’s growth. Our data platform integrates seamlessly, performs reliably, and delivers at industry-leading speeds.

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10 Ways Your Financial Firm Can Use Data Analytics to Level Up

A comprehensive data analytics strategy gives financial firms a competitive edge, helping them inform decision making, drive overall financial performance, and improve business outcomes. The fact is, all types of financial firms, from banks to investment companies, are finding new uses for analytics while optimizing proven use cases. You’re probably leveraging analytics for some use cases, but there’s more you can do. Embedding analytics processes across your organization can deliver more value—and deliver value faster. Here are 10 ways to benefit from data analytics at your financial organization:

1. Deliver personalized financial services

Tailored offerings are mandatory for success in financial services. Connecting customer and financial data for analytics gives you a better understanding of each customer’s financial goals, risk profile, and financial status. You can then deliver personalized offerings to meet customers’ unique needs. Offerings can include cash back on credit cards, or personal or business loans at a favorable interest rate. Meeting each individual’s financial needs improves customer experiences while enabling cross-selling opportunities that improve revenues.

2. Gain real-time insights

Real-time insights position your firm to seize opportunities or enable you to take action if you spot a potential problem. For example, you can deliver special terms on a loan or make a limited time debit card offer while someone is browsing your site, or take immediate action if you suspect fraud on an account. For example, credit card companies use real-time analytics to approve transactions exceptionally fast and also analyze purchases for fraud. Likewise, in stock trading, every millisecond can make a difference when buying or selling at market prices, making real-time insights invaluable.

3. Improve operational efficiency

Analytics let you automate processes to improve operations. Manual and repetitive tasks can be automated to minimize human intervention and errors while speeding up processes. For instance, onboarding customers, approving loans, and processing transactions are common tasks ripe for automation. Data analytics can also play a key role in digital transformations, enabling digital processes and workflows that make operations more efficient. For example, Academy Bank transformed operations in a hybrid environment, saving more than four hours of manual data entry per day and developing new online services to improve the customer experience.

4. Manage risk across the enterprise

The financial industry is constantly exposed to risk—market risk, credit risk, operational risk, and more. Data analytics offers early insights into risk, giving you time to proactively mitigate issues before they become full-blown problems. Applying analytics lets you better understand and manage risk to protect your organization against potential losses while supporting financial stability. For example, analyzing customer data, historical data, credit scores, and other information predicts the likelihood of a person defaulting on a loan.

5. Inform financial investment decisions

In an industry as complex as financial services, you need the ability to analyze vast amounts of data quickly to understand trends, market changes, and performance goals to guide investment strategies. Sophisticated data models and analytic techniques offer granular insights and answers to what/if scenarios to inform investments. In addition, financial analytics can help you strategically build a diversified investment portfolio based on your risk tolerance and objectives.

6. Ensure accurate regulatory reporting

In your heavily regulated industry, timely, trustworthy reporting is critical. Compliance with myriad rules that are constantly changing requires analytics for visibility into adherence and to create accurate compliance and regulatory reports. Data analytics also helps monitor compliance to identify potential issues, helping you avoid penalties by ensuring operations follow legal protocols. Plus, analytics processes offer an audit trail in reporting, giving your stakeholders and auditors visibility into how the reports were created.

7. Enhance fraud detection and prevention capabilities

Fraud is ever-present in the financial sector—and fraudulent tactics are becoming increasingly sophisticated and harder to detect. Your business must be able to identify fraud before financial losses occur. Analytics, including advanced fraud detection models that use machine learning capabilities, help identify patterns and anomalies that could indicate fraud. Analytics must also prevent false positives. For example, analysis must be able to distinguish between a customer’s legitimate purchases and fraud to avoid suspending a valid customer’s account.

8. Create accurate financial forecasts

Forecasts directly impact profitability, so they must be trustworthy. Data analytics can deliver accurate forecasts to help with budgeting and investments. The forecasts predict revenue, expenses, and organization-wide financial performance. Having a detailed understanding of finances enables you to make informed decisions that increase profitability. Data-driven predictions also inform scenario analysis, which lets you evaluate potential business outcomes and risks based on assumptions you make about the future.

9. Determine customers’ credit scores

Credit scoring is essential in finance, allowing banks and other lenders to evaluate a customer’s creditworthiness based on their credit history, income, and other factors. Analytics can determine if the person is a good credit risk, meaning the customer will repay the loan on time and manage their credit responsibly. Analytics can be used for any sort of financing, from offering a loan to raising credit card limits.

10. Understand customer sentiment

Like other industries, financial services firms want to understand the perception customers and the public have about their business. That’s where sentiment analysis helps. It interprets the emotions, attitudes, and opinions behind social media posts, reviews, survey responses, and other customer feedback. This lets you better understand customer feelings about your brand and services. You can determine if your customer and business strategies are working, and make improvements accordingly. Customer sentiment also serves as an economic indicator, giving you insights into how optimistic customers are about their personal finances and the overall economy.

Unify Data for Financial Services Use Cases

Data analytics has become an essential part of decision making, automated processes, and forecasting for financial services. The insights help firms like yours stay competitive and proactively adjust to changing market conditions and customer needs. New analytics use cases are constantly emerging. One way to capitalize on these use cases is to have all data unified on a single, easy-to-use cloud data platform that makes data readily available for analysts and anyone else who needs it. The Actian Data Platform does this and more. It connects all your data so you can drive financial services use cases with confidence and enable enterprise data management for financial services.

Related resources:

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Business Analytics vs. Financial Analytics: What’s the Difference?

There’s a saying that data is just data until it’s analyzed. It’s the analytics that turns data sets into insights to guide businesses. It’s important for data users and decision-makers to know which type of analysis will deliver the answers needed. Two common types of data analytics are business analytics and financial analytics. While they can overlap in the data they use and even have common goals—business and finance are often intertwined—they also have distinct differences and drive different use cases. These analytics inform business decisions, drive organization-wide improvements, and identify solutions to ongoing and emerging challenges. By contrast, financial analytics offer insights into current and future financial operations, allowing organizations to take actions that improve financial performance and boost profitability.

It’s best to think of business analytics and financial analytics as complementary rather than working against each other. For example, analyzing sales data benefits both the business and finance. Let’s look at how business and financial analytics are different—and why those differences are important:

Business vs. Financial Analytics

The most obvious difference between the analytics is the areas of focus. Business analytics looks at overall business performance and daily operations to inform decisions on strategies, processes, problem-solving, and other business-centric areas. These analytics enable a range of improvements and benefits, such as charting an accelerated path to reaching business goals and measuring progress along the way. Financial analytics focuses on all financial aspects of the business, which can range from determining profitability to measuring top and bottom-line performance to informing budget decisions. Applying these analytics also helps organizations predict cash flow, measure business value, and determine how changes, such as launching a new product or improving sales by a certain percentage, will affect profitability. Knowing the type of insights that are needed will determine which analytics need to be performed.

Business analytics are generally more widely used throughout an organization than financial analytics. A business analyst is a general term for anyone who performs business analytics. Other positions using business analysis can include data scientists, citizen data scientists, machine learning and AI developers, operations teams, chief data officers, and others across the business. Financial analytics falls under the domain of CFOs and their departments. They perform analytics to build financial forecasts, identify potential risks, predict future financial performance, and provide other financial information.

Business analytics helps with workflows, process improvements, and organization-wide decision-making. For example, analytics can identify inefficient business processes, such as bottlenecks that slow down operations, and determine the best avenues for improvement. With financial analytics, organizations can make more accurate financial forecasts and investment decisions. In conjunction with predictive financial models, the analytics can answer a variety of fiscal-related questions, such as determining a customer’s lifetime value, understanding how churn and net new customers impact revenue, and measuring ways that initiatives like implementing environment, social, and governance (ESG) best practices influence profit margins.

Each type of analytics has specific questions it answers for what/if scenarios as well as providing insights into business or financial areas. Business analytics typically informs overall business strategies, such as determining if there’s a gap in the marketplace where the company can introduce a new product line, and help the business prioritize goals. Financial analytics also helps inform strategies, but those strategies are tied to goals for the chief financial officer (CFO) and the broader financial team. These analytics uncover insights related to business expenses, the organization’s overall financial health, and investments, including investments in research and development.

For the best analytic results, all relevant data should be integrated and made available to analysts. This means business and financial data can be brought together for insights. Specific business insights can be uncovered by analyzing data related to operations, customers, supply chains, products, sales, marketing, employees, sales, and other business areas. Financial analytics looks at financial and economic data, which is needed for any fiscal planning. Current, accurate, and appropriate data is required for each type of analytics to deliver relevant and trustworthy insights.

Simplifying Data Analytics

There are many types of analytics in addition to business and finance, such as sales analytics, compliance analytics, and risk analytics. They all have several things in common—they use data to inform decision-making, predict outcomes, identify and mitigate problems, and drive improvements. Regardless of the analytics being performed, organizations need a modern platform that can scale to meet growing data volumes, make integrated data readily accessible to everyone who needs it, and is easy to use for all analysts. The Actian Data Platform delivers these capabilities and more. Whether analysts want a deeper understanding of the business or are taking a deep dive into finances, the Avalanche platform makes it easy to connect, manage, and analyze data. The easy-to-use platform brings together all data from all sources to deliver the analytic insights decision-makers and stakeholders need.

Related Resources you may Find Useful:

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