You are sitting at home minding your own business when you get a call from your credit card’s fraud detection unit asking if you have just made a purchase at a department store in your city. It was not you who bought expensive electronics using your credit card – in fact, it has been in your pocket all afternoon. So how did the bank know to flag this single purchase as most likely fraudulent? Read More- http://bit.ly/2iaufVW
Artificial Intelligence Could Dig Up Cures Buried Online
THIS SUMMER, RIVA-MELISSA Tez was searching online for research that might help her father. He would have gone into a coma after suffering a stroke, and she wondered what the latest recommendations said whether playing music to him in his native language could keep him connected to this world, or if giving him Prozac could boost his chances of recovery as it had done for mice in a study last year. Doctors are so busy saving lives, she thought, that they couldnt possibly keep up with all the papers published every day.Read More on http://bit.ly/2fxn6i8
With the Rio Olympics, this has been yet another big year for an increasingly data-rich sports universe. Previous high-profile global sporting events like the 2014 Fifa Football World Cup and the 2015 ICC Cricket World Cup had already pushed the boundaries of digitalization in the sports world. Not many, for instance, would be aware that a leading German technology giant helped the German football team win the 2014 Fifa World Cup in Brazil through the use of a software system called ‘Match Insights’.
Similarly, regular sporting events like the English Premier League (EPL), Formula One or the Indian Premier League (IPL) have begun storing and leveraging big data to win the ultimate prize in their respective sporting discipline.
We often hear about how data is being used by players to boost performance or even in the form of wearable technology. But data, as we show below, can be used in many other ways:
Game day analysis: Coaching and scouting of current or new talent, recommending the ideal winning strategy or team combinations, pinpointing the movements of a particular football player or identifying the kind of balls that a batsman in cricket is susceptible to get out to, are some of the common practices of leveraging data—a strategy also highlighted in the popular Hollywood movie on baseball, Moneyball.
Broadcast sports production: This is probably the most recurring and commonplace data-driven activity in sports considering we see it all the time. The data generated for spectator programming such as replays, player/team statistics, cool game facts and other relevant real-time data sourced by broadcasters adds more value to viewers.
Broadcast and digital distribution: This is the distribution of content across multiple broadcast channels and the use of social media and consumer-generated content in the broadcast. It also includes the use of metrics to identify what is “making waves” and on what basis to charge advertisers.
Advertising: As is the norm these days, the promotional and commercial aspects around huge sporting events, irrespective of whether it is on-field and broadcast, as well as the associated metrics are big opportunities to use advertising to not only build a brand, but to also build a dedicated following as well.
Fans: Undoubtedly, the lifeline of sports. They could be spectators at the event, or even viewers at home or on the go. With popular media platforms like Hotstar and Cricinfo.com providing real-time match coverage and score updates, the consumption of sports has evolved beyond the traditional TV.
These cases simply enforce the mind-boggling reach and multiplicity of digital technologies today—for every 100 people in the world, there are 95 mobile phone subscriptions, 40 Internet users and 25 social media users. What took the telephone 75 years to reach 100 million global users since its invention has taken the likes of Facebook (4 years 6 months), WhatsApp (3 years and 4 months) and even popular gaming applications like Candy Crush Saga (1 year 3 months) far less time to reach the milestone.
The opportunities, therefore, are aplenty in the sporting arena for technology companies to partner and work with various sports leagues and teams across the globe to create and provide a unique experience for fans.
Consider the case of an Australian technology firm Ecal that has signed a deal with the English Premier League for the 2016/17 football season. Ecal will create a ‘Premier League Digital Calendar’, which will allow fans across the globe to integrate interactive schedules for their favourite teams into their personal devices—be it mobile or desktop.
A lot of the programming expenses are going into live sports because of the interest and value in watching it live. The building of die-hard fan communities to interact is another aspect with immense revenue generating potential. In fact, an upcoming area for fan experience is monitoring fan behaviour—the more reactive a stadium or team is to the live fans, the more passionate the experience becomes and the more likely it translates into extra revenue for the sporting team.
It’s worth noting, though, that irrespective of how far team owners go to create that exceptional experience for their fans, it’s the match day performance of their team that counts the most. In sports, you want to acquire the best player just like in any other business where you want to hire the best worker. It begs the question of how to do that in order to get the best outcome and optimal performance. The answer is data.
Anil Valluri is president at NetApp India and SAARC.
Big Data and Analytics for Banking- Innovators are using big data and analytics to sharpen risk assessment and drive revenue.
In the 1980s and 1990s, IT systems transformed virtually every single bank process. Today, banks have that rare opportunity to reinvent themselves again with data and analytics. Every single major decision to drive revenue, to control costs, or to mitigate risks can be infused with data and analytics, says Toos Daruvala, a director in McKinseys New York office.This will be a differentiator for some period of time. In this video, Daruvala explains how three diverse banks are using analytics to gain an edge in a cutthroat environment by improving risk assessment and predicting customer behavior. What follows is an edited transcript of his remarks.
Drive revenue, reduce risk
Data and analytics provides a few very big opportunities for banks. At some level, actually, you can think of it as a way to transform the institution, much the way in the 1980s and 1990s and early 2000s IT and systems basically transformed every single bank in terms of how you apply IT to different business processes from a cost-reduction standpoint, from a revenue-generation standpoint, etc. I think in the same way, you will find data and analytics transforming institutions.
What you see is that almost every single major decision to drive revenue, to control costs, or to mitigate risks can be infused with data and analytics. Typically, the near end applications that we see are in marketing and customer sales leads and lead generation and on risk management. Both are disciplines that have historically used information pretty well. But I think we are now at the next frontier in terms of using both data and analytics to drive revenue generation through marketing, through next-product-to-buy, through lead-mining models like that, as well as to drive better risk decisions.
Let me give you a couple of examples of real world situations where I have seen this applied quite powerfully. There was one large bank in the US, which had not refreshed their small-business underwriting models in several years. Certainly not post the crisis. And they were getting worried about the discriminatory power of these models.
The Gini coefficient of their models—which is just a measure of how powerful a model is in terms of its ability to discriminate between good risks and bad risks—was down in the sort of 40- to 45-percent range. What these folks did was developed a 360-degree view of the customer across the entire relationship that that small business had with the bank, across all the silos. Not easy to do; easy to talk about, not easy to do.
And then what they did was selectively append third-party data from external sources, trying to figure out which of those third-party pieces of information would have the most discriminatory power. And they applied the analytical techniques to redo their models and essentially took the Gini coefficient of the models up into the 75-percent range from the 40- to 45-percent range, which is a huge improvement in the discriminatory power of those models.
Another bank that we were working with was in the developing markets, where data to begin with is pretty thin on consumers. And they decided that they would try to actually get data from the local telco. The paying behavior for the telco is actually a great predictive indicator for the credit behavior with the bank. And so they bought the data, appended that to the bank data, and again had a huge improvement in underwriting.
Another institution, a marketing example, where we ended up using, again, that 360-degree view of the consumer and then appending some external data around social media to figure out whats the right next product to buy for that consumer and then equip the front line to make that offer to that consumer when they walk into the branch or when they call into the call center. And the efficacy of their predictor models on next-product-to-buy improved dramatically as well.
So these are examples of things that you can do. And part of the reason why this is so important is that in the banking world, of course, in the current regulatory and macroeconomic environment, growth is really, really, really hard to come by.
A huge differentiator
I think the advanced-analytics opportunity quite simply is an opportunity to redefine the playing field. I think some banks will seize that opportunity and will be able to truly differentiate themselves using data and analytics. Examples I would use are some banks that in the early days used ATMs to truly create competitive advantage for a few years. Some banks in the early days of the Internet truly created a differentiated position online for themselves.
I think that is the way to think about it. Data and analytics will be a differentiator for some period of time, with other banks playing catch-up. So there is an opportunity here if you choose, as an institution, to be thoughtful about where you make some smart, targeted investments. Do you use data and analytics to drive growth in the business, to drive better risk behaviors in the business, and to reduce costs across the business? And that can be a huge differentiator.
Source: MCkinsey ( http://www.mckinsey.com/business-functions/business-technology/our-insights/how-advanced-analytics-are-redefining-banking )