8 ways big data is revolutionising fintech
Big data is huge in fintech. In fact, it’s so ubiquitous that when you’re looking to upgrade your skills or your business practices, it’s hard to know where to start. Here are eight of the most practical use cases where big data can quickly generate big wins.
- Fraud Prevention
The surge in theft of both data and money by fraudsters is driving financial institutions to use every possible tool to protect themselves. Small institutions are most likely to be targeted.
Combining financial data analytics with machine learning analytics can defend against fraudulent activities like rogue trading, speculator trading, and regulatory violations. For example, companies can strengthen their security systems with regtech (regulatory technology), which includes:
- intelligent Anti-Money Laundering (AML) applications.
- end-to-end fraud protection via a single-risk platform.
- blockchain for safer direct transactions.
- machine learning for customer identification, communication monitoring, and detecting unusual actions.
- Risk Analysis
Big data enables companies to manage risks with next-gen risk detection systems that offer deep risk analysis.
Running virtual simulations of potential risks can help organisations prepare. For example, risk analysis can be used to predict whether a bank customer is likely to repay a loan by creating a risk model based on the customer’s transaction history.
Data-driven risk assessments can also improve audit management, where risk assessment plans are a vital part of the process.
- Customer Behaviour Analysis
Text analysis, data mining, and natural language processing can be used to crunch large amounts of customer behaviour data and draw insights, allowing organisations to:
- cross-sell more effectively
- estimate and increase the lifetime value of each customer
- reduce below-zero-value customers
- segment customers for more targeted selling
- build customer trust
- model behaviour for both real-time and predictive analysis
- Credit Allocation
Data analysis can be used to assess a client’s credit history and create an automated credit score without any human labour. Machine learning can also cross-check their behaviour with credit-scoring models and produce an accurate prediction of their likelihood of repaying a loan.
- Predictive Analytics
By extrapolating from current data, predictive analytics can be used to forecast upcoming trends and predict the future behaviour of stocks and market prices. This helps users to develop more informed financial strategies and make better investment decisions.
- Product Improvement
Data-driven product improvement strategies enable companies to adapt to the demands of today’s rapidly changing markets, understand where products need improvement, modernise them, and introduce them to the market at the precise time when they’ll be best received.
- Algorithmic Trading
70-80% of all trades in developed markets are now algorithmic–they use algorithms to solve trading issues by analysing variables like volume, timing, and price. This percentage is expected to reach 95% within a few years.
Algorithms, with a little human supervision, can spot trading opportunities humans can’t. They can act and react at lightning speed, spotting missteps and correcting decision-making models on the fly.
- Personalised Marketing
By analysing customer preferences and trends, financial organisations can segment customers and speak to each customer’s wants and needs with personalised marketing campaigns. This improves user experience, conversion rate, customer retention, and ROI, boosting the organisation’s financial performance.
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