Posts tagged ‘fraud detection’
As I said in my recent post on the U.S. health care system, the U.S. cannot continue at its current spending rate. Certainly, the McKinsey Study (and many other publications) makes this very clear. Now, you and I may have very different opinions on how this can be fixed depending on our political leanings, etc., but ignoring this problem is not going to make it “go away.” Lucky for us, McKinsey takes a look at how big data and analytics can alleviate health care costs in some very promising areas. Whether you share my views on health care reform or not, it’s clear that we need to figure out how to align health care policy and regulations with economic incentives designed to move the industry towards a more collaborative, data sharing approach. To me, this is the “real” health care debate!
If you are a card carrying member of the big data community but don’t know much about the state of health care data, you may be surprised at just how antiquated data collection and usage is. If you have been steeped in the health care industry (and not just in the health care reform debate), this will be old news: health care data is not systematically collected, stored, and used. It is the only trillion dollar-plus industry in the U.S. without a modern information technology infrastructure. Look at is this way: while medical technology has advanced, much of the infrastructure that supports it is paper-based. For example, most of our medical records are stored in files (and I mean physical files) at various hospitals and doctors’ offices. Whenever you see a new doctor or go to a new clinic or hospital, chances are you fill out the same forms (again and again) documenting your medical history, risk factors, allergies, etc. (more…)
I have a confession to make. I am in marketing and as such, my profession often tries to make the complex sound simple. We look for sound bites that will help folks to understand, in five words or less, a complicated story, process, service, or feature. I am sure that you are familiar with this as it is everywhere—politicians, news organizations, television, radio, and companies of every size struggle to reduce the very complex down to the very simple. Quite often, something gets lost in the translation.
This is the case with real-time analytics. As you may recall, in a previous post Terence pointed out that “real-time,” as it is applied to analytics, does not meet the computer science standard. Am I splitting hairs? Yes and no. Unfortunately, we (as in marketers in the big data and big analytics space as well as all the tertiary spaces such as business intelligence, data warehousing, etc.) coined the term “real-time analytics” when what we really meant, for all intents and purposes, was pretty darn fast analytics. (By the way, if you want to make developers go crazy, say real-time and then sit back and watch as they carefully, rationally, try and explain why there is no such thing as a real-time analytics system.) (more…)
Mary mentioned our new fraud detection capabilities in her last post. Our primary fraud detection mechanism uses what is known as Benford’s Law. Benford’s law, also known as the first-digit law, is a neat little algorithm that checks to see if the digits in a randomly selected subset from a large group of numbers match the experimentally determined probabilities for a particular digit.
While powerful, you have to be careful that your problem really fits within its constraints. Benford’s law works best on:
- Highly variable numeric data (such as stock prices, global sales figures, tax returns and not IQs, body weight, or most things that follow a normal distribution)
- Data that is truly numeric and not an identifier (for example, a price versus a Social Security number)
- Large data sets (if sampling a larger set, make sure to use a truly random sample) (more…)