A wave of innovation in the financial sector has gained traction in recent years as finance companies and banks realize the unlimited benefits of digital technologies. Driving this change is the evolution from a product-centric to a customer-centric business model. The finance sector is thus integrating sophisticated technologies and cutting-edge Data Science tools into the processes for better services, lesser operating costs, minimum exposure to risks, and a higher ROI for keeping shareholders happy. Ultimately the mainstays of the finance sector are brand image, spotless reputation, and revenues.
At the same time, the 2007-8 financial crisis exposed the weakness in traditional finance tools and operational techniques that led to increased financial regulation, penalties, and limits on risk-taking.
The challenges of finance companies are both in their products and services. Data Science in finance is transforming the way information is extracted to identify irregularities and fraud, make accurate predictions, anticipate risk, decide on the investment roadmap, and make a data-driven decision for customer satisfaction.
So if you have a passion for data and want to pursue a career in one of the most dynamic and lucrative sectors, opt for a Data Science Certification to launch a career in the financial industry.
Applications of Data Science in Finance
The financial sector handles massive streaming data in real-time, and the consequences of errors or fraud can be enormous, including penalties for non-compliance.
Constantly shifting landscapes in payment methods, increased sophistication of hackers, and an increased focus on financial inclusion, has driven unique innovations in finance for a business edge in a highly competitive industry. Banks are working to build a culture of innovation besides gaining higher profits in a highly competitive industry. Moreover, the finance industry revolves around customers, and Data Science plays a significant role in customer-intelligence-supported value-added services.
Risk Assessment begins with examining structured and unstructured data pipelines to identify, evaluate and measure potential risks and associated damage. It includes analyzing real-time and historical data with minimal latency, assigning credit scores, and managing risks across the organization. Big Data analytics and machine learning are applied to build credit and market risk models to enable banks and financial institutions to manage risks and take timely actions and data-driven decisions.
Big Data frameworks are the go-to Data Science tools to improve risk scoring models. Typical use cases are monitoring credit scores from different credit bureaus and using algorithms to determine the risk score of a customer’s loan repayment behavior, any active loans, debts, mortgages, etc., to customize the interest rate and thresholds. Data Science thus helps the banking and financial services more profitable, as they can stay within specified risk thresholds while maintaining customer satisfaction.
Data Science leverages Big Data across various social and eCommerce platforms and the Internet to track and monitor the digital footprints of customers for useful information.
Machine learning algorithms working with high-performance computing help parse customer behavior and understand relationships between new macro factors and customer transactions. Such insights help the fintech or financial firm allocate credit and fine-tune traditional credit scoring models to predict whether a customer can pay back a loan and when.
Financial firms are customer-driven, and the personalization of services is increasingly becoming the differentiator between competing financial businesses. They deal with massive customer data, including data created by websites and customer service chatbots. Firms leverage this to categorize users based on demographics, age, location, transaction patterns, similarity in behavioral patterns, etc., by applying unsupervised machine learning techniques and Data Science algorithms.
Predictive models leverage customer analytics to measure the customer’s lifecycle value, reduce churns to cut down the losses, and make personalized offers and upsells for a personalized customer journey. Other advantages of consumer analytics are increased marketing efficiency, migration to profitable segments, customer loyalty, and retention. It helps make strategic data-driven business decisions and offers recommendations based on their banking, investing, and transactional activities.
The financial sector deals with monetary figures and real-time transactions, often cross-border. It increases their exposure to fraud. Cyber crimes, credit card fraud, and identity fraud have increased the risks of fraud across departments, both customer-facing and internal. The steep rise in fraudulent transactions and consequent loss in brand image and losses from financial crime has made it imperative for finance companies to tackle fraud detection.
Data Science tools and Big Data software have helped banks and financial institutions monitor transactions and check fraud using machine learning algorithms to learn from past data. It has helped detect anomalies by finding hidden correlations and creating highly efficient systems for timely alerts and fraud prevention.
While traditionally, fraud detection uses rule-based models to identify unusual transactions, it also flags legitimate transactions based on broken rules or sudden voluminous transactions. However, the application of Data Science builds machine learning algorithms to process massive datasets and discover the likelihood of fraud to restrict such fraud on time and avert financial loss. So banks and financial firms create systems for fraud prevention, such as credit card fraud, speculative trading, regulatory violations, financial irregularities, financial crime in online purchases, or insurance claims, and take informed decisions.
Today, trading and investments are powered by automated financial advisories or Robo-advisories for personalized financial services. It depends on the ability to make accurate predictions.
Algorithmic trading has disrupted financial markets with complex mathematical models, lightning-speed computations, and algorithms that allow financial firms to trade in real-time. It implements Data Science to implement algorithmic trading by analyzing the underlying data streams.
As AI systems get better with iterative learning, banks are looking at Data Science and AI for a competitive edge, as milliseconds can make a difference in high-frequency algorithmic trading markets.
The seamless 24/7 integration of data across multiple devices, channels, and points of purchase equips banks with rich data about customer behavior. Banks want to use this data to provide more relevant and targeted offers, advice, and rewards. Data is collected in real-time across the customer journey.
Data Science is used for customer segmentation to power sales and marketing decisions. Promotions and marketing campaigns are customized to make personalized recommendations to customers.
Mitigating Financial Crime
From sniffing out money-laundering schemes to detecting suspicious transactions and triggering red alerts, Data Science is core to financial businesses. In recent times where governments are tackling counterterrorism through controls and following the money trail of suspicious individuals or transactions, Data Science learns from the behavioral patterns of users and compares millions of transactions to distinguish between legitimate and fraudulent transactions and identify anomalies and fraud. Data mining, sentiment analysis, text mining, machine learning techniques, and predictive analytics are Data Science methods used in finance. They help identify suspicious activities such as large single-day cash deposits to set off alarms for instant decision-making, thus saving billions while conforming to regulatory compliance.
Forward-looking banks, finance companies, and fintech firms use Data Science to serve customers and mitigate fraud. Data Science is fast becoming the bedrock of tech disruptors, AI algorithms, and analytical tools for improved customer service and financial products.
With Data Science revolutionizing the financial sector with some of the best salary packages, there is no time like the present to upskill in Data Science.