Data science. Machine learning. Artificial intelligence. We’ve all heard the buzzwords over the last few years, but what trends in data use and analytics should we expect to see in 2019?
Will it be the year when these powerful data tools begin to find their feet in financial services? Will we see organizations tackle ongoing industry challenges—such as data access and real-time analytics—head on? Perhaps there’ll be an increase in industry partnerships where finance organizations turn to fintech partners to solve data problems?
The simple answer is that all of the above are likely to take leading roles in technology exploration this year. But, I don’t expect you to just take my word for it. I’ve scoured the web to see the areas industry leaders predict data science will expand this year and looked at how these advances could impact the financial services sector. I found three incredible blogs and six predictions that I think are worth sharing.
The first blog, the Top 10 Retail Banking Trends and Predictions for 2019, written by Jim Marous over at the Financial Brand, is a fascinating read and included two predictions for data science in financial services that I found particularly interesting:
Prediction 1. ML and AI to drive near-instant decisioning
“2019 will be the year that machine learning and artificial intelligence really begins to make a difference — not just in bank efficiency but more importantly in the customer experience. The cost and ease of implementation has decreased dramatically, putting technology within reach of even smaller financial institutions. One place where it will be most apparent is in small business lending, where automated ‘near instant’ decisions will become the norm for the majority of loans.” – David Kerstein, Founder of Peak Performance Group
This prediction particularly resonated with the Provenir team as it echoes a lot of the conversations and goals that we see in both fintech organizations and large financial institutions, particularly around the area of near-instant decisions. As businesses move towards a truly digital experience, the reliance on data tools, such as machine learning, to control risk will increase dramatically. With advances in data analytics tools, will 2019 be the year when near-instant decisions become the norm and not the exception?
Prediction 2. Big data will power hyper-personalized banking
“In 2019, we will see financial institutions moving from the traditional product and service offering—one size fits all—to a truly hyper-personalized type of banking, where banks provide customers with tailored products that better fit the customers’ short and long-term goals. All this heavily powered by big data and ML/AI.” – Sofia Flores, Product Manager for Retail Banking at Backbase
While business is moving towards a customer centric approach—with many organizations already able to make personalized offers to customers—there are a number of technical challenges that prevent the hyper-personalized approach discussed above. This type of hyper-personalization is only possible when a business can utilize data to truly know their customers across all parts of the customer journey. To use data science to better understand their customers they’ll need to address the common problems that stand between them and data, such as siloed data, integration delays, and orchestration challenges.
The second article that I found inspiring, written by Gregory Piatetsky, over at KDnuggets asked industry leaders to predict 2019 Key Trends in AI, Data Science, Analytics. Again, there were two predictions that I thought could have a huge impact on the financial services industry:
Prediction 3. Technology that facilitates democratization of data science
“One of the major developments for 2018 is the democratization of data science. From cloud technologies, which allow people to give resource-intensive big data and AI applications a whirl without having to build a data center first to tools like Kubeflow which bring scalable data science to folks without infrastructure expertise. This trend towards tools that make data science accessible to everyone will accelerate even more in 2019.” Cassie Kozyrkov, Chief Decision Engineer, Google Cloud.
The ‘democratization of data science’, has incredible potential for the financial services industry, where finding data scientists with industry expertise can be a challenge. This isn’t about replacing data scientists, but more about empowering business users to have access to these powerful tools that can provide valuable business insights. This is especially relevant in risk decisioning, where giving business users the power to use technology with less reliance on IT teams can improve access to data, ensure teams are solving the right problems, and improve the customer experience.
Prediction 4. Improved model deployment rates
“According to the Rexer Data Science Survey, only 10-15% of companies “almost always” deploy results and another 50% only deploy “often.” That leaves 35% – 40% of companies that only occasionally or rarely successfully deploy analytical models. I have encountered some organizations that say their successful deployment rates are less than 10%. Of course, there is no economic value to an analytical model that isn’t deployed. Companies will need to measure and improve their deployment rates in 2019.” Tom Davenport, is the President’s Distinguished Professor of Information Technology and Management at Babson College, the co-founder of the International Institute for Analytics, a Fellow of the MIT Initiative on the Digital Economy, and a Senior Advisor to Deloitte Analytics.
At Provenir, one of the challenges that we help businesses overcome is difficulties in model deployment, and it’s something that we agree will gain more attention as businesses push forward with their digital agenda. Having the ability to create, test, and deploy a new model in an efficient period is essential in a world where consumers want instant payments, loan approvals in seconds, and to be onboarded in minutes.
The third article, contributed to Forbes by Gil Press, specifically explores the future of AI in 2019 and pulls together an incredible 120 insights from industry experts. While there were many, many quoteworthy contributions, these two stood out for me:
Prediction 5. Data challenges driving partnerships with fintech organizations
“Because companies are recognizing that AI cannot be built without high-quality data, they will increasingly turn to specialized providers that sit on crucial data resources to help them understand their unstructured data. For example, Bloomberg is building NLP libraries that are specific to the financial domain”—Gideon Mann, Head of Data Science, Office of CTO, Bloomberg
While this prediction looks at cross-industry use of AI there is one point that I think is absolutely relevant for financial services—data access. I absolutely agree that there is a growing need for high quality data to power data science programs within the finance industry, especially as more businesses explore Neuro-Linguistic Programming as an alternative method of assessing lending risk. But, even if data is available, it doesn’t mean that financial services organizations will be able to easily integrate it into their existing technology stack. So, to truly use these data tools to their optimum level, organizations will have to find a simple way to make integration to data sources efficient, and for many this will involve partnering with fintechs.
Prediction 6. AI and ML will help humans, not replace them
“We predict artificial intelligence will become more prominent in the insurance industry in 2019 as more insurtech companies and carriers utilize the technology in their customer experience strategies. At the same time, we also don’t believe that AI will replace the human insurance agent in the new year or in years to come. Though machine-learning models can be used to help agents become better advisors to their customers, the human touch will always be important in insurance”—Jeff Somers, President, Insureon
While this prediction discussed the role of AI and ML in insurance, I think it applies to the financial services industry as a whole. There is still a huge amount of knowledge and understanding of human emotion that we have yet to pass on to virtual assistants, and when it comes to finances there are some circumstances where humans want to speak to another human. What’s fascinating here is that we can use technology to really hone our interactions with customers, for example, knowing exactly what loan amount they would qualify for as soon as we interact with them instead of placing them into credit prequalification buckets with wide ranges. Data science can give us the ability, with the right implementation and use, to create a truly customer centric approach with personalized products and interactions. This is something that I believe will become an expectation, not just desire, of customers as banking progresses in the digital world.
How to Integrate Data Science into your Risk Strategy
Join Michael Shurley, Provenir’s Director of Industry Solutions, for a live webinar on February 13th at 11 am ET where he will discuss the technology needed to implement data science in risk decisioning, why data science teams often solve the wrong problems, and why businesses need to fully embrace data science to drive success in their risk strategies. He will also share a demo of how the Provenir Platform empowers data science in risk decisioning and eliminates data access challenges.