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Warwick Analytics in the Press: Finance Digest, How Predictive Analytics is changing the Finance Ind

How Predictive Analytics Is Transforming The Finance Industry

Processes within the financial services industry have become more automated in recent years. Processes such as customer services, spotting fraud and error, credit scoring and processing insurance claims are becoming more automated thanks to predictive analytics and machine learning (sometimes referred collectively as “AI”). And there is plenty of evidence that automation is becoming more widely known and accepted. Indeed 

The Financial Times has reported that the CFA Institute is updating its Certified Financial Analyst (CFA) exam so that starting in 2019, it will include questions about artificial intelligence (AI), big data and robo-advice, reflecting the growing impact of machine learning on the finance industry.

But whilst it all sounds encouraging, the roll-out isn’t as fast as many commentators expect or hope. According to a PwC Report 2017, only 30% of large financial institutions have invested in AI. This could be because much of the core technology hasn’t changed for decades, be it decision trees, neural networks, Bayesian statistics etc. The practical application is also limited because of the amount of work that data scientists need to spend building and then curating machine learning models. Most importantly however, the complexity of the financial services industry is increasing, driven by ever-changing consumer behaviour and expectations, new disruptive businesses, the sophistication of fraudsters, the explosion in data (including a lot of unstructured data) and indeed more regulation.

This complexity is slowing the progress of AI, as it requires more data scientists to train and deploy the algorithms and cleanse and handle the data. This is particularly true when the processes and datasets involve unstructured data such as text:In a recent survey carried out by Warwick Analytics on AI in text, surveys, social media and queries/complaints were identified as the most common datasets being used. However, most analysts (53%) using text analytics wanted more insight from it and nearly half of those (23% out of the 53%) were not satisfied with the output of the analysis that they were getting.

Customer expectations are changing, with more interactions across more channels, and larger, richer datasets and increased need for personalisation and segmentation.Criminals are also getting more sophisticated with their own technology and organisation and financial institutions needs to move more swiftly to stay ahead.In the larger institutions there are still many humans interacting with customers and making operational decisions in front-, mid- and back- office which could be done more effectively by, or with the aid of AI (sometimes called “Robotic Process Automation” or “RPI”).

These new challenges within the industry require more sophisticated technologies and solutions and long-established business models are being disrupted by fintech newcomers that are creating new services, disintermediating the traditional value chain, and driving down costs. 88% of incumbents are increasingly concerned they are losing revenue to rival innovators (PwC Global Fintech Report 2017).

The good news is there are AI solutions such as PrediCX appearing which help to automate data science itself, and minimise the input (both the time and skill-levels) necessary from humans to adopt and deploy.



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