Let the machines do the work!
Machine Learning
Machine learning is not new. Today’s computing power and data collection techniques have simply driven it to the forefront. Why is it exciting now? Accuracy. For example, credit decisioning is much more precise and predictive meaning that a better model for repayment means you can do better business. From fraud, video at ATMs, optimization, personalization, we are taking data and turning it into personalized, self-service experiences.

By definition, machine learning is anything a computer is doing that a human could be doing. From analytics perspective, it gets us information from data. From generic models, neural networks, etc., today we can let data do the job of converting data into the information we need to do business.

 Machine learning automates analytical model building. It is all about data and is part of the artificial intelligence family. Examples of ML applications include: credit scoring and best offers, fraud, customer segmentation, online recommendations, customer lifetime value, targeted acquisition, retention and attrition, real-time ad placements, natural language processing, network intrusion detection and predictive asset maintenance.

We now have the potential to do things that we couldn’t do before; make decisions we couldn’t make before. Why? Because machine learning is more accurate than traditional analysis, enabling us to create a perfect credit model, for example. Think of its power as your investment bankers precisely price a large trading portfolio on-demand, with the model taking into account pre-deal risk, stress testing, etc. to get a true value for the portfolio.

In risk management, machine learning is helping analysts identify trends faster; news sources to surface relevant information more quickly; and finance and treasury professionals identify maco-economic factors to facilitate risk scenario planning.
Artificial Intelligence in Industry
In broader terms, SAS is using its artificial intelligence capabilities to analyze huge volumes of data in industries such as the auto industry turning service repair data into cost savings; electronics industry in creating personalized experiences at retailers; healthcare to forecast healthcare demand for nursing resources; insurance industry to detect and prevent fraud before losses occur; and, in financial services to engage customers and prospects with better offerings and service.

As AI assumes a more prominent role in banking, it behooves marketers to think about how it can enhance their role as shapers of the customer experience. To better understand what comprises AI, click the blue box here for a 'primer on AI." 
Click here for a PRIMER on AI for bankers & marketers
The Analytical Marketer

Data integration and marketing data architects will emerge as new and vital roles in marketing organizations as artificial intelligence and machine learning-based marketing tools analyze data and customer behavior, make recommendations and predictions, and get smarter based on the data and information fed into them.


Visiting SAS gave us a birds-eye view into how we need to view the intersection of technology with traditional marketing metrics and optimization. We peeked through the window of their AI processes and tools. As Matt Brumbaugh, Fifth Third marketer, said, “AI is murky still for us as marketers. What SAS did for me is to crystalize how AI is applied to data modeling and intelligence tools. It gave me a greater appreciation of how AI is being evaluated for its reasonableness and its ability to help marketers discern the underlying drivers of behaviors.”


We are no longer limited by human capabilities, time and resources. While humans still are in the driver’s seat with respect to marketing strategy and creative, machines can analyze, process and deliver personalized content at a massive scale. AI and machine learning make hyper-personalization a reality.

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