To navigate the immediate obstacles, financial institutions must assess short-to-medium-term financial risks and adapt to new ways of operating in a post-pandemic world. Data science can be a powerful tool in finance, aiding risk management and continuity planning so that the industry is better prepared when the next challenge arises.
A recent report from the World Economic Forum predicts that 463 exabytes of data will be generated daily by 2025. That‘s equal to 212 million DVDs a day, with an almost incomprehensible amount of actionable insights. Here are four key examples of how insurance, banking and investment companies can use data science to innovate the financial field.
According to the American Bankers Association, banking institutions prevented $22 billion worth of fraudulent transactions in 2018. Now, using solutions powered by machine learning technologies, the finance industry is aiming at real-time fraud detection to minimise losses.
Machine learning enables the creation of algorithms that can learn from data, spot any unusual user behaviour, predict risks, and automatically notify financial companies of a threat. Data science helps banks recognise:
Financial institutions are responsible for managing vast amounts of customer data – transactions, mobile interactions and social media activity. This information can be categorised as “structured” or “unstructured” – the latter posing a real challenge when it comes to processing.
Employing data science within finance helps companies manage and store customers’ data far more efficiently. Firms can boost profits using AI-driven tools and technologies such as natural language processing (NLP), data mining and text analytics, while machine learning algorithms analyse data, identify valuable insights and suggest better business solutions.
The financial industry faces potential risks from competitors, credits, volatile markets and more. Data science can help finance firms analyse their data to proactively identify such risks, monitor them, then prioritise and address them if investments become vulnerable.
Financial traders, managers, and investors can make reliable predictions around trading, based on past and present data. Data science can analyse the market landscape and customer data in real time, enabling financial specialists to take action to mitigate risks.
Data science can also be used in finance to implement a credit scoring algorithm. Using the wealth of available customer data, it can analyse transactions and verify creditworthiness far more efficiently.
Data science is a powerful tool for helping financial institutions understand customers. Machine learning algorithms are able to gather insights on clients’ preferences, to improve personalisation and build predictive models of behaviour. Meanwhile, NLP and voice recognition software can improve communication with consumers. Thus, financial institutions can optimise business decisions and offer enhanced customer service.
Studying behavioural trends allows financial institutions to predict each consumer’s actions. Insurance companies use consumer analysis to minimise losses by defining below zero customers and measuring customer “lifetime value”.
The use of data science in the financial sector goes beyond fraud, risk management and customer analysis. Financial institutions can harness machine learning algorithms to automate business processes and improve security.
By using data science within finance, companies have new opportunities to win customer loyalty, safeguard their profits and stay competitive.
Originally published here.