Finance industry processes are becoming more foolproof and efficient due to automation. There is plenty of evidence to showcase the growing impact of Machine Learning (ML) and its wide acceptance across various use cases in capital markets.
There has been no time like now for the adoption of ML predictive analytics models by financial institutions. The increasing complexity of the finance world is driven by rising consumer expectations, the increasing sophistication of fraudsters, and data explosion.
Predictive analytics models are helping make credit scoring, fraud detection, claims processing, and customer services more seamless. Financial institutions are seeking more help from data scientists in building and curating ML models for predictive analytics to stay afloat amidst the changing market dynamics.
What is Predictive Analytics?
Predictive analytics uses statistical data modeling techniques to analyze historical data and make business decisions based on identified trends. Previously, machine learning and predictive analytics had never converged, and the latter existed much before the former.
It was the Mathematician Alan Turing who first harnessed predictive analytics to decode encrypted German messages during World War II with the Enigma Machine. Present-day advanced predictive analytics makes use of predictive models which are typically made up of machine learning algorithms. Over time, these models can be trained to respond to new and varied datasets as new business requirements strike in.
Use Cases of Predictive Analytics in Finance
Predictive analytics can aid in a variety of finance processes and offer insightful data interpretations with the application of predictive models. Here are four use cases that implement predictive analytics:
1)Fraud detection in online transactions
Predictive analytics-based software analyzes banking transaction data with pre-trained algorithms. Based on this data it scores a transaction for the risk factors. Data experts or scientists at the bank would label a large volume of transactions as either fraudulent or legitimate and then run them through an ML model. The model is then able to pinpoint future fraudulent transactions.
For instance, a fraudster makes a transaction for a product that the account holder is unlikely to purchase. The illegal transactor also happens to be in a geographical location different from the account holder at the time of purchase. The machine learning model recognizes these inconsistencies and flags the transaction as suspicious.
The client bank may then decide on the right legal recourse, which may include temporary suspension of the account. Multiple channels involved in payment processing can be monitored with such fraud detection software.
2)Enriched credit card scoring
Credit card scoring is a procedure performed by lenders to ascertain an individual's creditworthiness. Legacy credit card scoring applications continue to be used in Asia Pacific internet financial institutions. These applications suffer from limitations such as strict data assumption and incapacity to process complex data.
Several AI research papers propose an advanced alternative. This involves a predictive credit evaluation model based on an extreme gradient boosting tree (XGBoost) ML algorithm. This ML model predicts credit risk by mining internet data around a credit cardholder. It, then, repeatedly calculates income status, credit history, payment level, and other indicators to arrive at a credit rating. This helps lenders evaluate credit risk more accurately.
3)Upselling and cross-selling
Upselling and cross-selling are widely used methods to maximize profit and are growth strategies adopted by e-commerce giants such as Amazon. Upselling is when you recommend a higher-priced alternative to a customer's selected product while cross-selling is when you recommend a worthy addition or accompaniment to the product. Predictive analytics uses certain tuples of the customer's data to implement these strategies effectively.
Based on customers' data such as past purchase history, purchase power, demography, and frequency of purchase activity, the ML model segments them into four categories - promotion seeker, controlled spender, service demanding, revenue reversing customer.
Once the customer category is arrived upon, triggers are set at strategic points in the buyer's journey to direct them closer to upsold or cross-sold items. It is far more cost-effective to cross-sell and upsell, rather than wasting time and resources in generating a new lead for a sale.
4)Predicting stock price movements
Although making accurate predictions about the movement of shares in the stock market is impossible, we can make informed guesses based on past track records. A predictive analytical model using Recurrent Neural Network (RNN) and layered with a Long Short Term Memory (LTSM) network is what enhances the accuracy of results.
There have been past instances wherein stock market experts consulted with data scientists to build Technical Analysis (TA) models to predict a stock's price direction. An optimum predictive analytics model may implement RNN while utilizing some principles of TA to arrive at predictions.
If such models were 100% accurate, it would make billionaires out of people with the appropriate know-how but sadly, that is not the case. Such ML models need further fixes and enhancements before they reach a considerable level of reliability.
5)Claims Management
Claims management is hard on both claimants and insurance providers because of the sheer volume of paperwork involved and efficiently assessing the credibility of claims. Predictive analytics can help in the faster arrival at claims outcomes. Insurers can then provide the best insurance plans fitting the claimant's financial status and requirements.
Predictive analytics models can process claims and mine the claimant data, slowly learning details about each type of claim and user. These details may include the nature of the injury, treatment, aspects of the claimant, liability, solicitors required, and others.
They then identify claims that can be processed by bots and route only complicated or nuanced claims to staff for review. These models also make associations between different factors in the claims data helping insurers decide the allocation of resources based on triage. Additionally, insurers use these models for the early identification of potentially high-value losses.
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Conclusion
Predictive analytics and ML models, in general, could maximize profits and productivity for financial institutions. But, for properly integrating these solutions into everyday operations, they must have the right support architecture in place and high-quality data to feed these models.
As data continues to diversify and change, more businesses are implementing predictive analytics models in their financial processes. For implementing such models and get timely insights into your financial data, you can book a consultation with our AI experts.