The coronavirus pandemic and the global measures that have followed have created a perfect economic storm. The financial sector stands at the front line of a growing credit crisis, with banks trying to manage disruption and maintain strict compliance amid social distancing guidelines which are at odds with their processes. Then there are the extraordinarily low interest rates and increasingly cash-insecure consumers to contend with. Some of the biggest banking challenges posed by the pandemic are:
- Prioritising resources to cover the most critical business processes. Like many industries, the banking sector has found itself scrambling for answers and slow to make decisions on resourcing capacity, because it lacked an adequate data repository.
- Delivering financial services off-site. Some essential financial operations, like branch banking, treasury or settlements, can only be done on-site. And the lack of a comprehensive customer database has prevented banks from being able to promptly accept and process payments from different accounts.
- Dealing with the rising number of fraud cases. There have been numerous cases of critical data theft since COVID-19 first appeared. With rigorous data analysis, suspicious transactions could have been identified sooner and monetary fraud prevented.
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.
4 ways to harness data science within finance
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.
1. Detect and prevent fraud
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:
- Fake insurance claims. With the help of machine learning algorithms, data provided by insurance agents, police, or clients can be analysed to spot inconsistencies more accurately than with manual checks.
- Duplicate transactions and insurance claims. Duplicated invoices or claims aren’t always sinister, but machine learning algorithms can distinguish between an accidental click and a premeditated fraud attempt, thus preventing financial losses.
- Account theft and suspicious transactions. Algorithms can analyse a user’s routine transactional data, then any suspicious activity can be flagged and verified by the card owner.
2. Manage customer data more efficiently
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.
3. Enable data-driven risk assessment
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.
4. Leverage customer analytics and personalisation
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.