Tag Archives: Decision Optimization

You need to find faster, smarter and more value-inducing ways to get from the data to decisions (and actions) rapidly. Analytics are key to that process.

This is the second in a series of blog posts on deposit price elasticity, focusing on the modelling data requirements. There are several different modelling techniques and approaches to measure deposit price elasticity, which is dependent on the actual business problem and model usage. The exact data requirements might need to be amended to account for the modelling technique, but a large number of data items are consistent across all approaches. As with all modelling projects, it is good to initially take a step back and think about what type of information you would expect to be predictive, impact deposit price elasticity, and make you move your savings across different products and / or financial institutions. We would suggest that considering the below fields would be best practice for a deposit price elasticity model development: Product details: Historical interest rate of the product(s) to be modelled, this would include retention... [Read More]

Many top financial institutions have begun using predictive modelling and optimization to improve deposit pricing. This requires an understanding of customers’ deposit price elasticity — how sensitive are they to pricing changes, and what is the relationship between price and demand at the customer, segment and portfolio level? I’m going to explore this topic in a series of posts, which should be useful both to deposit portfolio managers and analytics teams. To start with, let’s look at the basics. Price elasticity is the study of responsiveness, and how the demand of a product changes with respect to price (and/or the price of competitors). Understanding deposit price elasticity, or having models that predict this, means you can quantify: Impact of a product’s price change on the deposit product How competitor price changes impact a deposit portfolio Impact of changing macro-economic conditions, such as a change in central bank lending rate How... [Read More]

A lot of people may not know what decision trees are. Do they bear fruit? Are they hardy enough to survive inclement weather? Actually, decisions trees are an essential part of daily life. Everyone uses decision trees to plan their lives, whether or not they know it. Only we probably don’t think of them as decision trees. Should you change out of your pajamas? Will you ever find the thing you lost? Should you get a dog? Several daily life decision tree examples are posted on this site – these are intentionally funny, perhaps, but I think we can admit this stuff isn’t that far from the truth. Well, if decision trees power our daily decisions, how are businesses using decision trees to power their customer interactions? This was the key topic that my colleague Lamar Shahbazian and I recently co-presented in a short webinar, Unleashing the Power of Business Rules Development. While decision trees in business take... [Read More]

Innovation is in the plumbing of Yarra Valley Water, Melbourne’s leading provider of water and sewage services. The utility is committed to supplying recycled water to 100,000 homes, so people can continue to enjoy the natural beauty of the area without affecting drinking water availability. They’re also on the forefront of redirecting storm water to reduce pollution in the area’s scenic creeks and rivers. Their innovation is also making customers and businesses more efficient. Yarra Valley Water’s online portal for developers, easyAccess, helps expedite application approvals while reducing manual processes and freeing up employees to focus on more complex applications. easyAccess has been around since 1995; however, by 2011, the solution was unable to keep up with increasingly complex applications, and only 40% of each year’s 5,500 submissions were handled automatically — the rest required human intervention. Yarra Valley Water chose FICO® Blaze Advisor® Decision Rules Management System which enables its employees to... [Read More]

There’s little need to belabor the point that many businesses today generate more data in a single day than they used to in whole year. A few years ago storing all of this data at a reasonable cost was the biggest challenge. Newer technologies now solves for that. However, data by itself is neither useful nor meaningful. Simply sitting on a stockpile of data provides minimal value. The real value is to make your data “decision-ready.” Simply put to take data process, augment and analyze it to improve its immediate decision value. Whilst seemingly difficult, there is a tangible way to continuously get your data to state of decision-readiness. I’m not talking about a one-off project that takes a herculean kind of effort similar to the Manhattan Project. Rather I’m talking about systemic and automatable ways to achieve this goal. Questions to Ask Start out by asking yourself what problems... [Read More]

As we settle down with our families to enjoy the winter holidays, many of us will be celebrating various traditions. For many, the culmination of the season is waking on December 25th to the excited cheers of their children as they see their Christmas horde waiting under the tree.  These children believe that Santa Claus, despite carrying what must be a massive load, manages to fly on a sleigh powered by eight reindeer to every home on the planet in one night. The jolly old elf climbs down the chimney and places each present carefully underneath the Christmas tree.  The children wake up on Christmas morning and, ta-da, there are presents. As it turns out, there is a word for this belief:  Apophenia.  According to Wikipedia, Apophenia is defined as the experience of seeing patterns or connections in random or meaningless data. Apophenia was first used to define the “unmotivated seeing of... [Read More]

Not too long ago, deploying advanced analytics required massive investments in IT infrastructure and application software, creating a sort of “analytic elitism.” Over the last year we’ve been focused on democratizing analytics, so that organizations of all sizes can base their operational decisions on data. And to do this we needed to build our own cloud infrastructure.

But why? Why did we decide to build our own infrastructure rather than leveraging a hosted provider like Amazon Web Services (AWS)? This was hotly debated within our company and essentially it came down to three very important reasons:

Cost: While AWS, Microsoft Azure, the Google cloud and other cloud hosting providers garner significant attention and mindshare, first and foremost their value is speed of resource delivery. When and if a customer needs server and computing resources, the network for these services can be deployed and accessed in very little time. Speed is a critical value for hosting service customers. By partitioning and deploying compute resources in minutes or an hour...