Data Analytics Applications in Finance
Businesses and institutions gather raw data each time a new customer onboarding, makes transactions or engages with the platforms or on social media. But standalone raw data has no value unless analyzed for meaningful and actionable insights. Introducing a platform Kapiche.com to create customer insights reports allowing you to make the most effective decisions for your business by organizing, processing, and analyzing data. It does make sense businesses are using the voluminous data at their disposal to make decisions, roll out new products and improve services.
Data Analytics is leveraged in almost every business or organization for data-driven decision-making, modeling, and predictions. Although it is used in most industries, Data Analytics finds the most prolific use cases in Banking and Finance.
As a wannabe Data Analyst, you may consider taking a Data Science Certification to be relevant in the analytical field and be career-worthy in a high-value industry such as Finance. Upskilling in a data-driven domain can offer you many career opportunities and widen the scope of joining the highly lucrative Finance industry.
What is Data Analytics?
Data Analytics is a scientific and automated method of analyzing raw data to make inferences. It uses algorithms to extract the data from multiple sources and formats, make it usable and crunch the same for insights. It helps a business in its digital transformation journey, perform more efficiently, increase profits, enhance brand value and make strategic decisions that make its customers and shareholders happy.
Top 6 Data Analytics Applications in Finance
Data Analytics helps analytical teams in the financial sector gather the information needed to answer specific business questions, solve business challenges and forecast possible future financial scenarios using key performance indicators (KPIs).
Here are some of the popular applications in Finance:
1. Insurance Risk Modeling
The risks in the insurance sector are diverse and depend on customer behavior and age factors, among others. Risk Modeling predicts such risks and prioritizes them to determine whether to onboard the customer and the premium chargeable. It ensures that the insurance firms charge appropriately for the risks and do not incur losses.
As a potential customer’s risk profile determines the insurance premium, insurance companies use Data Analytics and algorithms to examine market trends, and customer behavior and assess the risk of asset or life insured. By using previous customer data, insurance firms can create risk management models.
2. Risk Management in Banks and Financial Institutions
Risk Management is one of the most key areas in Finance. With the digitization of businesses and cross-border transactions, risk perception has also changed. For instance, transactions with businesses based in sanctioned countries pose a high risk. Compliance with anti-money laundering and counter-terrorism financing regulations emerging more stringent, the burden on the finance sector has also increased with risks of massive financial loss or penalties from non-compliance. Data Analytics plays a huge role in defining risky customer profiles, transactions, and business activities to avert potential losses. Historical customer data and market financials are leveraged to analyze risks and build risk-scoring models for determining financial charges for its services. Risk assessment helps price financial investments and products like various financial loans and funds.
Historical customer data and spending patterns are also analyzed to determine the creditworthiness of potential and existing customers. This strategy is useful for onboarding new customers, giving loans, and determining credit limits since the organization’s safety, and ROI are of utmost importance.
3. Fraud Management
Fraud is of great concern in banks and financial institutions, where it occurs at customer touchpoints (by customers) and internal systems (by corrupt employees). Frauds lead to losses and may even lead to penalties. To detect fraud and prevent the loss, financial organizations have employed Data Analytics techniques. Previous customer data and spending patterns are crunched to create alerts whenever fraud occurs. It has made credit card fraud difficult as timely red flags stop fraudulent transactions. Algorithms use training data to alert and freeze customer accounts to limit losses and detect perpetrators. Further Data Analysis of transactions in real-time helps detect and avert fraud for timely management and action.
4. Managing Customer Data
In the Financial sector, data is “gold.” It is the historical data that helps model products and services. Efficient data management and storage systems for timely retrieval and processing in real-time are key to business success.
The financial data is diverse and voluminous: transaction data, personally identifiable information, location data, spending patterns, mobile interactions, e-commerce transactions, and so on. Financial Analysts leverage semi-structured or unstructured data and integrate machine learning techniques and various algorithms to extract customer data intelligence, often in real time. They transform data into business value for smarter data governance, improved financial products, and better customer service. Automated report generation for model creation is another way the customer data is leveraged for designing products, credit and risk profiles, etc.
Customer sentiment analysis from social media activities and spending patterns at online platforms also help generate insights on customers’ lifetime value, identifying the high-value and loyal customers, and so on. It is then used with predictive analytics to focus marketing campaigns on valuable customers for higher ROI.
5. Customer Segmentation
Customer data related to spending patterns, location, and demographics are leveraged to segment the customers. Some of the categories for segmentation include income, expenditure, location, age, racial profile, loan and investment patterns, return of loans, and high-value worthiness. Customer segmentation is used for personalized cross-selling, sending cold emails and mobile texts for customized offers, and other aggressive promotional campaigns to increase value during the customer lifecycle. It helps banks and Fintech firms focus on selling the right product to the right customer.
6. Algorithmic Trading
Historical data on the market, industry, company, and stock performance makes real-time analytics simpler and streamlined. Financial institutions and Fintech companies make real-time decisions based on this data. Where time is of the essence, being competitive means speed in real-time analysis. Data Analytics has ushered in a revolution in the trading sector with cutting-edge algorithmic trading. Most exchanges use high-performance computing to make instant decisions based on algorithms and live-streaming data. Massive data, including financial indicators, tweets, market trends, and global and industry news, are factored in for real-time and predictive analytics.
7. Feedback Management
The customer feedback mechanism is critical for improving performance, services, and products. It helps to tailor marketing campaigns and promotions and helps Banks and financial firms to develop in-demand products and keep their customers happy. Feedback from website forms, social media, and other feedback mechanisms is crunched to gain an overall perspective of the success of a product or service. The Finance industry is highly competitive, where feedback management and Data Analytics have the potential to create a business edge.
Summary
Data Analytics is here to stay. Businesses, big or small, must leverage Data Analytics to extract business value from the data and use it for competitive advantage. In particular, the Finance industry is witnessing wide use of Data Analytics, from onboarding new customers to internal systems for fraud management.
So if you want to be part of a dynamic data-driven ecosystem in a highly lucrative industry, opt to upskill in Data Analytics and watch your career grow.
If you are Data Analytics enthusiast looking for an introductory course, Simplilearn’s Data Analytics Free Beginner Course is the right place.
