Financial Services
How is data analytics used in finance?
Data is the most powerful tool that businesses have at their disposal today. Channelling raw data through data analytics not just helps businesses understand current market trends but also enables transformation of businesses by making it possible to foresee possible negative consequences of strategies. That is the power of data and every organisation possesses it.
Most business sectors have found that the use of data analytics drives profitability, improves efficiency and lowers costs. Personalisation of services is one of the significant impacts of data analytics and it helps businesses attract and retain customers. Used in functions ranging from launching new products or services, identifying locales or target audiences, to analysing various bottlenecks through patterns or trends, insights from data analytics help businesses make better decisions.
The finance industry deals with confidential and sensitive information. Even minor human errors or churns in the market can have serious fallouts. It is thus imperative for them to keep a close eye on the data generated on a daily basis. However, the raw data in the industry is extremely difficult to assimilate and use. Hence, data analytics plays a crucial role in finance, and it has been doing so for quite some time now. Apart from contributing to profitability, efficiency, and cost efficiency, data analytics in finance helps businesses drive digital transformation and adherence to regulatory compliances.
Why data analytics in finance?
The finance industry is deeply affected by many global events such as the pandemic, convergence of industries, and rapid technological evolution driving digitisation. The profitability of these businesses depends a lot on eliminating risks and closely following and interpreting various market trends. Financial Data and analytics helps institutions turn massive amounts of unstructured customer and transaction data into valuable insights. These insights help businesses understand many key business aspects.
This industry also needs robust enterprise fraud management solutions to safeguard reputation and business continuity. Humans are always prone to error and oversight. Hence it makes good business sense to use data analytics in finance as it helps proactively identify suspicious or fraudulent transaction patterns to mitigate fraud risks consistently and objectively. Besides, it is a highly competitive market, and survival is tough. Whether banking or insurance, customers look out for better, more convenient, or relatable services. Data analytics in finance aids intelligent data-driven decisions to discover new markets, design new products and services, become more operationally efficient and manage regulatory compliances. Let’s now look at how the banking and insurance industries, two key finance industries, use data analytics in finance.
Data analytics in banking
A bank’s success depends a lot on its risk management abilities. Whether loans or investments, data analytics helps banks better analyse their credit risks through risk modelling. These models help banks implement fraud detection and management measures and assess operational and liquidity risks in-depth to safeguard their businesses.
On the performance side, banks can become hyper-productive* by assessing their KPIs through various visual metrics to understand which aspect needs more attention. In-depth sales and revenue performance analysis help make better and informed decisions to drive growth through data analytics.
Customer analytics help banks understand various trends to personalise their services and utilise digital marketing solutions to suit customer needs. It also helps them identify new business models to generate more revenues. Personalised services help banks retain their customers and acquire more new customers. Chatbots, virtual assistants, recommendation engines, and customer lifetime value predictions are outcomes of data-driven personalised services. Overall, customer analytics helps banks drive exceptional customer experiences through digital interactive solutions resulting in long-term gains.
Data analytics helps immensely with even simple decisions that can significantly impact revenues. For example, understanding which locales need more ATMs, how many counters or tellers should operate at a specific time, etc., can help banks enhance their operational efficiency. Faster responses to queries on regulatory compliances are another key benefit of using data analytics for banks.
Data analytics in insurance
Insurance is a highly competitive market, and customers are demanding and fickle. They expect the best and will switch to another insurance provider with just one bad experience. Insurance companies can access new data sources to finetune their target audiences and services to retain their present customers. Predictive analytics can help them design better-suited products, and customer analytics will help them engage better with their customers using intelligent customer care solutions. For example, arriving at the optimum premium for a new offering or rewarding low-risk customers with lower premiums are possible with data analytics.
Risk profiling is a vital factor that impacts insurance companies and their revenues. Data analytics helps them with in-depth risk profiling, leading to informed underwriting decisions and better product pricing. For example, while generating a motor insurance policy for a person with a history of bad driving, a data-driven analysis can suggest a higher premium. Mitigating claims fraud is another key area where data analytics helps insurance companies. Detecting frauds can cause a delay in claims processing leading to customer dissatisfaction. However, by deploying claims fraud detection systems, insurance companies can ensure speedy processing and avoid risks. Additionally, predictive analytics can help insurance companies to arrive at new products or identify regions for specific products and services.
In the age of digitisation, the finance industry is relying heavily on data analytics to sustain and grow their business. It also helps them innovate and find better ways to strengthen their customer experiences.
*For organizations on the digital transformation journey, agility is key in responding to a rapidly changing technology and business landscape. Now more than ever, it is crucial to deliver and exceed on organizational expectations with a robust digital mindset backed by innovation. Enabling businesses to sense, learn, respond, and evolve like a living organism, will be imperative for business excellence going forward. A comprehensive, yet modular suite of services is doing exactly that. Equipping organizations with intuitive decision-making automatically at scale, actionable insights based on real-time solutions, anytime/anywhere experience, and in-depth data visibility across functions leading to hyper-productivity, Live Enterprise is building connected organizations that are innovating collaboratively for the future.
