Financial Services

Artificial intelligence transforms an increasing number of domains, including financial institutions. This technology can drive operational efficiencies in areas ranging from risk management and trading to underwriting and claims.

Use Cases

Fraud detection

Predictive analytics enables financial organizations to detect fraudulent activities among a very large set of transactions. Possible approaches include the detection of outliers or the modeling of "normal" cases against "fraudulent" cases and using this model for new transactions to check if they fall into the fraud segment.

Churn prevention

Some customers terminate a contract for a wide variety of reasons. It’s good to know in advance for which customers this will happen with the highest likelihood in the near future. This is exactly the idea behind churn prevention: create predictive models calculating the probability that customers are likely to quit contracts soon so you can be pro-active, engage with them, offer incentives, etc.

Sentiment analysis

The idea behind sentiment analysis is to connect to thousands of online sources in the web, collect statements about your brands or products, and analyze them by means of text analytics with respect to their tonality or sentiment. You can identify how sentiment changes over time and measure the success of marketing or PR by this or you can get new insights about how to improve your products. If you combine this with network analysis you can even detect key opinion leaders and see how they influence their peer groups.

Trading analytics

If you are trading, building portfolios, or preparing deals, the natural idea would be to calculate the success rate of your decisions with help of predictive analytics. In general, you could analyze potential trade opportunities by looking at market data or inspect markets for trading activity which showing an emerging trend. Lately, many analysts combine more traditional methods like time series analysis with behavioral trading algorithms or even sentiment analysis.

Risk management

Data mining and predictive analytics can be used for solving multiple problems connected to risk management, including error detection and quantification, unit reviews or internal audits, detecting fraud, identifying suppliers with highest probability of failure, quantifying payment risks, and credit scoring.

Benefits

  • More customized and holistic solutions, which make money work harder and adapt as consumer needs change
  • Robo-advice, automated insurance underwriting and robotic process automation
  • Optimized product design based on consumer sentiment and preferences
  • Time saving
  • Consumer trust and regulatory acceptance