How Does a Finance Data Analyst Help CFOs Transform Their Business?

Table of Contents

Introduction

What if you get the power to see everything, every time from everywhere?

What if you could monitor each customer interaction, keep track of every financial transaction happening anywhere in the world, get updates on each new product, and double-check every moving part of the supply chain?

Imagine what happens if you can process all this information instantly and utilize those insights to build new products, improve customer experience, and identify and stop fraud.

Living a life of the imagination is one thing and bringing that imagination into reality is another. A Finance data analyst helps you get your imagination into reality.

A finance data analyst helps you see the future and allows you to predict upcoming issues, spot possible frauds, prevent customer churn, and anticipate and prevent financial failures.

A financial business has nerves made of data and insights flowing into veins. Your data is your primary asset, and you already own it. A finance data analyst helps you capture, analyze, and use it to transform your business.

What is a Finance Data Analyst?

A finance data analyst is a professional helping financial institutions manage and utilize data for making high-precision business decisions. These professionals are skilled in performing an in-depth analysis of the economic data to gain insights and assist financial institutes in implementing indicative actions and enhancing performance.

A financial analyst plays a crucial role in forecasting the future and keeping tabs on sales and profits. Productivity, business methods, and much more. Besides, a finance data analyst also plays a pivotal role in presenting insights using customized dashboards and intuitive data visualizations.

Leverage Data Analytics in Finance to Revolutionize Your Business

Data analytics benefits almost all industries. It is particularly true for the finance industry, which often has a massive amount of raw data to analyze. Data analytics in finance is helping global financial institutions strengthen their customer relationships, offering personalized products, avoiding scams and frauds, and gaining a competitive advantage.

Here are some of the ways finance data analyst helps you transform your business,
Make data-driven decisions:

• If used wisely, finance data analytics helps decision-makers make evidence-based decisions rather than following their instincts or depending on intuitions.
• Finance data analytics promote better decision-making by converting structured and unstructured data into actionable insights.

Discover new opportunities:

• The hidden insights help in finding future markets and better areas of investment.
• Finance teams are empowered to collect all the necessary information required to get a clear view of major KPIs, such as the revenue generated, payroll, net income, profits, sales, etc.
• Achieve growth by implementing strategic pricing models and indulging in data-driven marketing initiatives.

Build a Smarter Organization:

• Predict the change and anticipate the impact of the economy, market, and regulatory issues on existing business strategy.
• Utilize advanced analytics, new BI tools, and data analytics technologies to handle and analyze large volumes of data.
• Strategize for the future and prepare for the worst-case scenarios to ensure minimum impact on productivity and growth.

Manage risks and regulatory compliances:

• Leverage data analysts to ensure completeness, accuracy, and easy availability of data sources.
• Manage data compliance risks with proper data governance and data security policies.

If you are a CFO who wants to transform your business and build a data-driven finance organization, Hire a Data Analyst now!

Challenges in Finance Industry Addressed By Finance Data Analysts

There are several challenges in the Finance industry preventing them from implementing robust data analytics strategies. Here are some of the common challenges addressed by finance data analysts,

• Data silos:
  The majority of financial institutions have data stored at different sources. Usually, their data lives in various systems, databases, applications, cross-border servers, etc. All these disparate data sources prevent them from cross-referencing the data or building multidimensional dashboards. A finance data analyst addresses the challenge by unifying all data sources and creating a central repository to store and analyze data in real-time.

• Semi-structured data:
  Financial institutions often receive data from many external sources; usually, the data from these sources need to be structured. Frequently, semi-structured data like JSON is beneficial for real-time data streaming. However, finance data analysts empower financial institutions to ingest alternative data quickly into the central data platform for proper and timely analysis.

• Inadequate data governance:
  Giving access to data without implementing proper data governance strategies is risky, non-compliant, and might lead to inaccurate insights. Finance data analysts implement adequate and reliable data governance frameworks to manage data effectively. It helps finance companies stay compliant, maintain data quality, minimize risks, and keep data security at par.

• Augmented analytics:
  Finance executives can only make evidence-based decisions due to accurate insights and poor analytics. Finance data analysts facilitate competent decision-making by eliminating human and financial, or transaction process errors using autonomous extract, transform and load, ETL, data transfer, independent data models, and comprehensive data security. Augmented analytics helps finance executives in every aspect of decision-making, detect several impacting parameters, and use predictive analytics to identify and stop frauds before occurring.

• Personalization:
  A finance data analyst bridges the gap between a consumer and a finance institute by making them realize their client’s requirements and prioritize them. Personalization allows banks to segment customers and offer improved financial solutions to meet their precise needs. With data analytics in finance, CFOs are empowered to transform business-driven to customer-driven models. Analytics makes it possible to customize products, personalize services, and achieve a milestone in customer relationships and customer experience.

• Intelligent insights:
  Data analytics in the finance industry has already revolutionized how stock markets work. Finance data analysts who possess the required skills of Machine Learning engineers can help you build models that analyze historical data and find the factors that improve decision-making for investment. ML models generate intelligent insights using predictive and prescriptive analytics models to define rules for implementing AI. Intelligent insights are known to double profits, reduce customer churn, and increase overall customer value.

Data Analytics in Finance Use Cases

The banking and insurance sector is the first industry to leverage data analytics to go from pillar to strength. However, the finance industry is constantly evolving and is the first to embrace change open heartedly.

If you are a CFO and want to understand how to use finance data analytics to transform your business, you need to understand the use cases first,

Consumer Analytics

As per our discussion with 5LetterWords– For any financial institution, it is very much necessary to understand consumer behavior if they want to make the right decisions. And the best option to understand their customers is – Data. Finance data analysts use customer segmentation to break down consumers according to their age, location, relationship with the bank, insurance companies, credit cards, pensions, funds, etc.

Customer segmentation helps profile customers in a highly efficient way. These profiles provide valuable insights for personalizing products, targeted marketing, customized approaches, and enhancing customer experience.

Consumer analytics, in particular, helps banks to understand customer lifetime value. Based on their past, present, and future interactions, financial institutions can use them for mutual benefit.

Financial Risk Assessment and Management

Financial risk assessment and management identify potential threats and protect organizations from them. There are different financial risks, and a finance data analyst covers them. For example, credit risk (predicting if a customer will default on the loan or card payment) and market risk (predicting the trends in the market industry and analyzing if they will stay or will live for the short term). Apart from them, there are other financial risks, such as legal risks, inflation risks, and so on.

A financial risk assessment and management model identifies risks, monitors them, and prioritizes the ones needing immediate attention. Finance data analysts leverage ML models to identify, monitor, and prioritize risks by automating the model and minimizing human error. To create an automated risk management model, they consider a considerable amount of external data, including market data, social media data, and historic threat detection and pattern identification data.

Fraud Detection

With increasing automation, a large number of online frauds are taking place. Securing financial institutions and their customers is the main motive behind fraud detection. Some typical financial frauds are credit card fraud, insurance claim fraud, organized crimes, hacking, etc.

Finance data analysts leverage AI, real-time analytics, and advanced data mining techniques to detect anomalies and unusual patterns suggesting possible frauds. A specially designed algorithm alerts the finance authorities upon seeing such suspicious behavior and automatically blocks such activities to avert fraud.

Going a step further, finance data analysts create ML algorithms and train them to identify malicious activities at a granular level. Banks can stand on their toes during hacking, cyber attacks, data thefts, or organized crime.

Personalized Services

When physical banking shifted to online banking, the consumer and financial institution relationship became much more transactional, losing its personal touch. Data analytics in finance is helping banks to keep that personal touch and remain relevant in the digital banking era.

Personalized services focus on customer care and enlightening the customer experience. Personal data is used to create customized services divided into three parts.

• Prescriptive personalization – It uses past data and preferences to understand customer needs. A rule-based ML algorithm responds to customer queries and looks after their needs personally.
• Real-time Personalization – It uses past and present consumer data to personalize customer experience as the transaction occurs. It takes place in real-time and syncs with customer queries or needs.
• ML Personalization – It uses past, present, and future data to present solutions to tailor solutions according to consumer needs. A personal finance advisor is an excellent example of ML personalization that advises consumers on their spending habits and investment portfolios.

Product Development and Recommendations

Fintech providers are aggressive users of data analytics in finance. Although many financial institutions have launched their digital wallet, many still focus on other areas, such as AI-driven trading apps, lending software, mobile payment systems, insurance products, and many more.

Finance data analysts offer exciting product development innovations and customization according to customer needs and credibility. From customizing loan offers, mutual funds, and credit card offers to insurance products, consumer analytics, and personalization further help develop or customize products relevant and effective for cross-selling.

The finance data analyst uses ML algorithms and AI to analyze customer needs in real-time and recommend or offer a customized product to them suiting their needs.

Conclusion

Frequently Asked Questions (FAQs)

What is data analytics in finance?

Data analytics in finance is a process leveraged to convert structured and unstructured data into insights, which ultimately assists in making informed decisions. It helps analyze key performance indicators against a few industry-specific KPIs for determining growth and future strategies. It also allows finance executives to identify fraud and manage data governance.

Why is data analytics in finance so important?

Using data analytics in finance is to design a business strategy through reliable and factual insights. It helps the finance industry get a clear picture of financial data, transactional data, and customer behavior and get detailed insights into key market trends necessary to remain competitive.

Data analytics in finance helps organizations to make informed decisions, find new growth opportunities, personalize customer experiences, optimize prices, and do much more.

How is data analytics used in the finance industry?

Data analytics in finance is used for myriad purposes. However, here are some of the ways data analytics is used in finance,

• Predictive sales analytics
• Client profitability analytics
• Product profitability analytics
• Cash-flow analytics
• Value-driven analytics
• Shareholder value analytics
• Fraud analytics
• Risk detection

How do banks benefit from using analytics?

There are almost endless ways to apply data analytics in the banking industry. Banks use it to enhance customer experience, improve customer service, develop customer-centric products, optimize processes, automate systems, reduce costs, maximize productivity, and so on. When it comes to data analytics in banking, your imagination and technical infrastructure decide the limit.

Is a finance data analyst different from a data analyst?

Yes! A finance data analyst is different from a data analyst.

A finance data analyst uses financial data for analysis, identifying trends, predicting the future, and helping financial executives make informed decisions. The role of a finance data analyst is limited to the finance industry.

On the other hand, a data analyst performs the same duties with a myriad of data. They analyze data that may or may not have to do anything related to investment and banking. The role of a data analyst is not industry specific as they study numbers, figures, etc.