This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
For example, Budgeting and establishing operating procedures could be core activities here; Staff – which contains a collection of core work activities that support staffing activities within a business area. Once fully documented, the value chain represents the transformation team’s recommended work environment.
High-quality data is indispensable for informed decision-making, operational efficiency, customer satisfaction, regulatory compliance, and innovation. Decision-makers use data to analyze trends, understand market dynamics, and forecast future developments.
Influenced by this community, Accenture Applied Intelligence* has developed a fairness tool to understand and address bias in both the data and the algorithmic models that are at the core of AI systems. Step 2 and 3 occur after a model has been developed. The model falsely predicted that the person had low creditrisk.
Develop the right metrics. When I discuss this with executives, they often point out that the lack of highly developed metrics is both a function of the relative immaturity of Big Data implementations, as well as a function of where in the organization sponsorship for Big Data originated and where it currently reports. Insight Center.
Think of the colleges that are increasingly able to identify students at risk of dropping out and intervene before they do. Or lenders’ enhanced abilities to gauge creditrisk. They need to be thinking about how — and how much — they will develop and integrate predictive analytics capabilities into their services.
And they have well-honed approaches for developing the requisite new skills in employees. Ash Gupta is President of Global CreditRisk and Information Management at American Express, and Guy Peri is Chief Data Officer and Vice President of Information Technology at P&G. Address applications that benefit you and the customer.
We organize all of the trending information in your field so you don't have to. Join 5,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content