- Risk Analytics and fraud detection
The first application of data science in the finance sector is risk analytics. This application analyses various data sources, such as chat logs and social media posts, to detect potential fraud or risk. It also helps identify trends among interactions to allow companies to take appropriate measures to avoid future issues.
The insurance industry is a leader this lead in this regard. Insurers analyse internal data, including call centre notes, voice recordings, social media data, wages, criminal records, bankruptcies, criminal records, and address changes to know if there are potentially fraudulent claims. The telecoms industry loses $38 billion to fraud each year, and it’s hoping to fight this by employing data analytics. To build more complete user profiles, companies like Turkcell analyse billions of daily call data records alongside other customer data. Turkcell detects irregularities more effectively and quickly by comparing a customer’s recent activities with its highly detailed profile.
Also, analytics help in healthcare to detect fraudulent claims. The British National Health Service (NHS) accumulated almost £100 million in potential savings following a reduction in benefit fraud and the risk of human error after deploying a new analytics infrastructure. In addition, online ticket exchange StubHub reduced online fraud by 90% in the digital space after implementing an analytics-based detection system.
- Real-Time Analytics
One of the most prevalent roles in data science is that of a data scientist specialising in analysing and modelling large quantities of data to find patterns. These patterns help to make more informed decisions and improve decision making.
The use of machine learning is an excellent example of how real-time analytics helps companies make improved decisions. Machine learning allows companies to analyse their past data and see any trends or correlations that would help them make better predictions about future behaviour. It uses algorithms from previous analyses to determine the probability of an event occurring using input variables such as time, location, weather, politics and other factors. Using machine learning, companies can predict what events will happen in the future based on historical information.
The predictive power of machine learning has helped some finance organisations reduce risk and increase efficiency by decreasing fraud rates and improving loan payments for clients.
Streaming analytics offers real-time anomaly detection mechanisms to help banks and other financial institutions safeguard themselves from fraudulent activities. Banks can easily convert their domain knowledge regarding fraudulent behaviour to real-time rules, use Markov modelling and Machine Learning to detect unknown abnormal behaviour, and use scoring functions to reduce the number of raised false alarms.
- Consumer Analytics
Consumer analytics enable organisations to understand what consumers want and how they want it delivered. Using data analysis, companies can provide better products that align with the needs of consumers. Customer analytics go beyond just making intelligent marketing decisions. It significantly impacts the organisation’s profit. A recent McKinsey survey shows that companies that extensively use customer analytics report 115% higher ROI and 93% higher profits. Today’s most well-known brands have a profound understanding of their customer analytics. Make giant stride with your company by creating a better experience for your customers. It would be best if you took the time to build a customer analytics stack.
Another application is risk management. Companies can identify potential risks and strategies for minimising them by analysing data. In general, this includes identifying predictive models such as algorithms that predict customer satisfaction or revenue performance to make better decisions about product development or marketing campaigns. You can collect and store customer data and use it for analytics to unlock growth here.
- Customer Data Management and Personalized Services
Customer data management involves collecting and storing customer information to make better decisions by understanding customer preferences and needs. By understanding what your customers like, you can offer them customised products, services, or experiences that they will enjoy. For example, one company provides a customised experience based on the time of day that they visit their website. They use machine learning and predictive analytics to determine which features people are most likely to click on to design their website with these features in mind.
Personalised services are another way companies use data science to provide personalised experiences. They are helpful for digital marketing campaigns or to improve customer experience online. Personalised recommendations tell consumers about products that may interest them in purchasing them without guidance from a sales representative or a company representative. With the Customer data platform, One-to-One Personalization Is Within Reach.
- Algorithmic Trading
Algorithmic trading can help to execute trades in the financial markets automatically. This process is by a computer algorithm and can allow for significant cost savings for firms that use it. For instance, Goldman Sachs has saved money by investing in its algorithmic trading platform developed in-house.
Goldman Sachs invested in analytics tools because Algo trading has increased hugely among their FX clients for the past five years, and they project that there would be ongoing growth in use.
Algorithmic trading has also helped beat the market and reduce risk. The technology will only grow as more companies implement it into their business model.
Finance finds new ways to turn data into value in the modern digital age. From risk analytics and fraud detection to real-time analytics, customer data management and personalised services to algorithmic trading, this article has discussed five areas where data science is helpful in the financial industry and assisted in gaining a competitive advantage.
Next week I will discuss the future of work, focusing on whether employees can do data science jobs remotely.
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