Financial Data Analytics

Table of Contents

Introduction

Financial services have been plagued with innumerable challenges that have been a cause of concern given several parameters must be in check. Artificial intelligence and machine learning have taken over the simplified forms of banking. This is where processes are automated and some of the biggest examples include customer servicing, credit scoring to spotting frauds. The age-old days of standing in ques to get a financial transaction done are past, and the due credit goes to data analytics that has aided the growth of the financial services from the word go.

The rapid technological changes have swiped the world where seamless user experience is the major takeaway while safeguarding from financial risks comes in a close second. The massive explosion in the user interaction across several channels has plummeted the processes of collating data that now acts as leverage gaining deeper insights into the behavioral patterns of the customers. The data so collected is utilized by the banks for customizing experiences and generating refined products which are directly proportional in boosting the revenues and decreasing the marginal errors.

Big Data in Financial Services

The Financial Services sector is becoming the most data-intensive sector, thus the impacts of big data in the sector are beyond estimation. Largely, the banks are driven by enormous customer data size which includes their deposit, withdrawals, purchases, payment, and KYC updating details. As a result, banks being product-oriented financial institutions need to include data science and analytics algorithms in their spectrum to utilize rich data sets. Moreover, the changing customer expectations and increased competitive battles amongst Fintech players have led to the heavy investment in data collection and processing technologies like Business Intelligence. The early investments in big data technologies have brought commendable growth to the financial sector along with gaining an edge over competitors.
Besides, the change in customers’ expectations and behavior there exist other prominent key drivers impacting the relationship between big data technologies and financial services which are as follows:

The technological evolution like rise in IoT, advanced authentication techniques, opens APIs, etc. in real-time.
Growing competition in the Fintech companies using big data techniques for new financial services.
Regulatory pressure from the respective authorities.
Increased concern for cyber security.
Urge to reduce operational and administrative costs.

Types of Data Analytics in Finance

The use cases of data analytics in the financial sector have been growing with each innovation and technological introduction. As a result, it has extended its wings to fit all the data analytics types of which three remain of utmost importance.

1. Predictive Analytics in Finance

Financial predictive analytics help company financial heads like CFO’s to identify trends. It further facilitates accuracy in the planning, forecasting, and decision-making processes of the organization. Thus, predictive analytics help to answer crucial questions relating to the future, demand, sale, income, expenditure, etc. Thus, on the basis of these precise answers, organizational strategies and budgets are formed. Furthermore, predictive analytics play a crucial role in streamlining routine processes too. Some of these processes and operations include:

Cash flow forecasting and maintenance
Evaluating the risk factors and implementing healthy management tactics.
Understanding the receivables aging.
Targeting a profitable customer base.
Measuring the periodic performance of the company.

2. Business Analytics in Finance

The concept of financial business analytics does not discriminate between any field and department. As a result, it is potent to deliver supremacy in the financial sector too. The model is largely based on revenue research and analytics market surveys which connect dots between expectations and actual performance of the financial sectors. Thus, business analytics facilitate the following function in the financial organization such as banks, insurance companies, securities, fintech, etc.

Helps to reduce employee turnover by analyzing their needs at the earliest.
Leads to significant cost-cutting in recruitment, marketing, operational, and sales department.
Analyses product quality, performance, and impacts on the end-users.
Facilitate glitch-free payment mechanism.
Mitigate the potential future risks.

3. Advanced Analytics in Finance

The advanced analytics features have strengthened the financial domain with its robotic process, informative visualization, and cognitive cloud computing, and accurate prediction. Thus, the ecosystem of financial advanced analytics leverages data benefits using three main components which act as its key drivers.

The advanced analytics tools add user-friendly filters along with pivot data in the system. They help to provide an in-depth analysis of key business variances and drivers.
Responsive self-service applications play a prominent role in forecasting market scenarios and lead to the development of cognitive insights.
Effective data automation feature of advanced analytics provides a comprehensive outlook to the financial business entities based on in-context datasets.

Applications of Data Analytics in Finance

Forecasts seem to have a new name and what better than data analytics to put the quandary to rest. An advanced form of predictive analytics sees predictive outcomes generated through the incorporation of several techniques that include statistical analysis, data mining, modeling to the use of automated algorithms. A strategy that ensures the finance processes are enhanced while optimizing the usage of resources and addressing the issues plaguing the business. A model that works on probabilities and offers insights delving deep into the existing data plumes.

Data analytics finds applications across multiple domains and a few of which are mentioned in a tabular format and explained below:

Metric Name	Application
Credit Scores	Statistical techniques are utilized to check the creditworthiness of an applicant.
Improvement of Supply Chains	Vulnerable to fraud ranking is done for vendors to ward off the inefficiencies in the supply chain context.
Detection of Financial Fraud	Fraudulent transactions, phishing, application frauds to card skimming are detected through machine learning techniques.
Customer Spending Behaviour	Future behavioral patterns of customers are evaluated through data analytics.
Prediction of Lifetime Value	The future relationship between a customer and a bank is gauged through data analytics.
Revenue Predictions	Revenue forecasts cite the sales, operations to the behavioral data for a customer.
Bank Collections	Portfolio segregation of customers.

Application of data analytics in calculation of the above metrics is explained in detail below:

1. Credit Scores

Financial lenders have come a long way in adjudging loan defaulters through the utilization of machine learning and statistical techniques. The analyzed data is further narrowed down to get a conclusion that corroborates the risk of lending and assists banks and financial institutions to downsize the potential risks. The narrowed-down approximation as represented specifies the credit score and bears testimony to the creditworthiness of an applicant. A predictive modeling approach incorporated through artificial intelligence decodes the likelihood of defaulting on credit obligations and is the best tool to figure out the defaulters in advance. This aids in the decision-making process for increasing and decreasing the term, interest rates to the loan value. While paving the way for reducing bad loans. A lender verifies the creditworthiness of an applicant through the credit scores. The higher the credit score, the more likely it is for the loan to get sanctioned.

2. Improvement of Supply Chains

Financial lenders face a huge dent in their profits owing to supply chain inefficiencies. This is where data analytics comes in listing the vendors in the context of ranking from vulnerable to fraud and maximizing efficiency in the process. While the equipment that is set to falter is recognized too.

3. Detection of Financial Fraud

Data analytics as a tool offers much more than a generalized assumption of customer behavior. The collated data gives a sneak peek into the future behavioral patterns of consumers. Data analytics can play a great role through product development in improving the levels of customer satisfaction which can potentially impact the ROI.

4. Prediction of Lifetime Value

Data analytics also aids in predicting how much profits can be reaped through the future relationship between a customer and a bank.

5. Revenue Predictions

Anticipating a given product’s future demand has never been easier. Data analytics assist a great deal in revenue forecasts through multiple strategies that target the sales, operations, and behavioral data of a customer. This is where data for past purchases is streamlined and overlaid to match the recent search trends generating better forecasts.

6. Bank Collections

The bank collections are an integral part since the list of default payments is long and data analytics comes as a boon. The predictions assist in distinguishing the portfolios and focus on segregating the high-risk customers from the zero-risk ones. Several strategies are chalked out for improvising the outcomes of such collections which see maximum channelization in one direction.

7. Wealth Management

In the era of changing market conditions, keeping the wealth healthy is the main aim of financial departments and companies. Thus, the emergence of the data analytics concept has brought better fortune to the domain. Financial data analytics empower managers with reliable data that facilitate quick operational and investment decision-making processes. The robotic process automation (RPA) feature of financial data analytics help to support effective risk management, liquidity cash flow, retirement plans, and wealth-related augmented market research.

8. Healthcare Finance

Using financial data analytics, healthcare analysts perform a variety of duties relating to budgeting and financing at medical care centers. It includes all the processes from preparing to analyzing the financial statements. Here, the data analysts focus on calculating and tracking the critical financial ratios for the healthcare practices. It includes inventory management, returns on investment, working capital ratio, fixed assets turnover ratio, etc. The role of financial data analysts stretches to the health insurance sector as well where it helps in determining the liquidity and payment pattern of the client.

9. Corporate Finance

Corporate finance is the field of financing where activities and transactions are driven towards the creation of long-term financial value for the firm. As a result, it is directly related to the monetary, financial, and investment decision-making process of the company. Thus, it is important to use accurate prediction and prescription using data analysts tools and techniques to ensure the longevity of the corporate financial decisions that lead to heavy investment.

10. Accounting

Accountants throughout the world use financial data analytics to identify areas of improvement based on uncovered data insights. It helps to manage and mitigate risks and the expected losses. On the other hand tax accountants make the use of analytics technology for determining the tax liability and get answers to complex tax queries which otherwise require excessive time, if done manually. Using the big data features, the analysts can manage both fixed and liquid assets along with keeping track of the growing debt and other liabilities. As a result, it helps to evaluate the overall performance of the organization.

Examples of Data Analytics in Finance

Data analytics works wonders giving the necessary leverage to financial institutions and garnering better business results. The highlight of which is neutralizing the negatives and working on an effective risk management technology that entails the best of future outcomes. Here we will analyze three major fields where data analytics have earned a name for itself.

Here we will analyze three major fields where data analytics have earned a name for itself.

Field	Application of Analytics
Corporate Finance	The generalized decision-making process to strategic planning sees the full utilization of data analytics. This is what decodes the answers to questions related to future sales, increase in spends to the behavioral changes of customers.
Healthcare Finance	Hospital finance is best controlled through data analytics that improvises multiple aspects related to the collection of debts, budget regulations to savings expectancy. Cash flows also feature in the list which can be subsidized through data analytics.
Auto Finance	Data analytics has several implications when it comes to auto finance and is best suited to curating personalized services that boast of regulated and controlled pricing. Better forecasts, data mining to optimizations ensure customer expectations are met with the most accurate matches through data analytics. This is done by keeping the price scenarios in sync.

Advantages of Data Analytics in Financial Services

Data analytics offers the best of product enhancements and much-needed insights that are relevant to the need and aids in keeping businesses one step ahead of the loop.

Some of the major advantages have been discussed below:

Monitoring accuracies – Data analytics as a tool is a denominator between reality and expectations and forecasts the gaps much in advance to ensure positive outcomes.
Mitigation of risks and better management of fraudulent activities – Threat detection in advance through data analytics ensures businesses have the time to work on mitigating the risks.
Seamless user experiences – A business’s strength determination assists in investing for causes that have the potential to maximize the satisfaction levels of customers and data analytics does this best.
Improvement of products – Giving shape to a product has been easier with the utilization of data analytics that aids in establishing prices to giving a makeover of the product and its final distribution.
Informed decisions – The end product can be matched to meet the customer’s demands and data analytics ensures the issue is addressed in the best way possible.

Financial Data Analytics Tools

The strength of financial data analytics lies in its effective tools. These tools vary depending on the application and nature of the work. The most basic and widely used financial analytics tools are Microsoft Excel, PowerPoint, and Word. These are used for creating financial models, presentations, reports, etc for the senior management or the clients. Besides, some data and analytics platforms are acting as the tools for financial data analysis namely Capital IQ. Pitch Book Data, Reuters, and Bloomberg. With the use of these data platforms, financial analysts can diversify analytical studies based on real-time information. Besides these Oracle ecosystems are the best-suited mechanism for facilitating financial management, modeling, and analytics. Oracle ERP is considered the leader of the Gartner magic quadrant whereas Oracle Essbase and Analytics provide machine learning and AI features to the financial data set.

Techniques Used for Financial Data Analytics

The full potential of financial data analytics can be chased with the right set of techniques. The choice of technique varies with the nature of the complexity and size of the data sets. Thus, make sure to understand the analysis feature of these techniques before it becomes the ultimate analytical format for the business finances. Largely, there are three most widely-used financial data analytics techniques which have dominated the financial ecosystem.

1. Finance Analytics Dashboard

It is a business intelligence tool that facilitates data visualization and reporting. Furthermore, it helps to synthesize financial data flow in developing better insights which help to improve profitability. As a result, the financial analytics dashboard encompasses multiple data sources to leverage accurate financial pictures. The sophisticated data analytics feature helps to plan and forecast financial budgets. Hence, with the use of financial analytics dashboard users can delve and develop better information.

Examples of Financial Analytics dashboards include:

CFO Dashboard
Financial Reporting Dashboard
Cash Flow Valuation Dashboard
CAGR dashboard
Actual v/s Forecast Expense Dashboard
Expense Detail Analysis Dashboard
Option Valuation and Greek Dashboard
Margin Analysis Dashboard
Risk Adjusted Performance Dashboard
FX Sales Analytics Dashboard
Profit Simulations Dashboard
Break-Even Analysis Dashboard

2. Financial Analytics using R

The widely-used programming language R plays a significant role in the developing financial softwares and applications which helps to develop statistical models and analyses thereupon. The well-written text using attributes of R focuses on simulating a better understanding of financial data. As a result; it helps to fetch attention from the financial market. The sensitive ecosystem of financing when blended with coding libraries of R helps to establish predictive financial elements. With the use of R calculation and analysis part of the financial data gets an immense boost. Hence, financial analytics using R brings smoothness in the complex financial calculation aspects.

3. Data Analytics using Microsoft Excel with Accounting and Finance Datasets

No doubt Microsoft Excel is the most widely used tool for finance and accounting which has made it the prominent choice of financial data analysts too. The large set of financial data, complex calculations, and analytical problems can be easily handled with the use of MS Excel. The statistical dashboard, availability of dynamic formulas, pivot table techniques are the main component of MS Excel which provides immense power to the financial and accounting domain. The upgraded Excel functionalities are always the lender of last resort, it means when nothing works excel works the best.

Data Analytics – The Road Ahead

Data analytics has steadily gained ground for itself where the possibilities are ever-growing. Financial services need to ensure stability and the market uncertainty that looms large makes data analytics a mandate for studying and analyzing future forecasts. A move that ensures development across several domains but should be undertaken adhering to the perspectives of the company in question. The future of finance is heavily reliant on the combination of automation along with human intervention to ring in an ethical ramp-up of services.

The same states analytics to take over four key domains mentioned below:

Acquisition of customers – Analytics can be combined with marketing capabilities to improvise the selling tactics through real-time tracking of preferences for each individual.
Forging relationships – Financial institutions can work on winning customer loyalty and appreciation. This can be done through developing products as per choice and needs to satiate a neutral customer to turn into a loyal one.
Customer retention – Data analytics ensures initiatives are launched that classifies customer groups according to their likes.
Services – Picking out the communication channels to serving each customer up to the mark is what data analytics determines and answers.

The road ahead is filled with possibilities and it is yet to be seen how future innovations with regards to data analytics mean for automation. Better facilitation and optimization of existing processes also count in.

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