Credit


Is artificial intelligence the future of finance?

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AI has also been the subject of a recent European Commission (EC) consultation document, to which CFA Institute submitted a response. This'training' involves using a large training data set that the computer algorithm can repeatedly go through (but typically with guidance and supervision) to learn through trial and error how to connect the input data (e.g., credit history, employment history, assets, purchasing history) with the desired output (e.g., the correct identification of a suitable risky portfolio). Although some attempts have been made to check the source code of algorithmic traders, the most effective protection against algorithmic errors are circuit breakers on markets that limit the amount of damage a failing algorithm can cause. Consider attending the CFA Institute European Investment Conference, held in Berlin this November.


Machine Learning Applications in Credit Risk

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Step 2: Assign every entity to its closest medoid (using the distance matrix we have calculated). If so, make this observation the new medoid. Model Validation • "Model risk is the potential for adverse consequences from decisions based on incorrect or misused model outputs and reports. " [1] • "Model validation is the set of processes and activities intended to verify that models are performing as expected, in line with their design objectives and business uses.


What Is Optimization And How Does It Benefit Business?

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For example, there is search engine optimisation (getting your website pages to the top of online search results), process optimisation (making existing processes more efficient), code optimisation (making your code run more efficiently) and then there is mathematical optimisation. In this blog post, we'll be focusing on mathematical optimisation: what it is, how it can be applied in making more optimal business decisions at a customer level, and specifically how it's applied in credit risk. As you can see, mathematical optimisation is already widely used to optimise business outcomes, maximise efficiency and increase profitability. We have put together three interactive examples – using the Optimisation Tool further down – showing how optimisation can be used to refine a credit lender's account level decisions.


Is artificial intelligence the future of finance?

#artificialintelligence

AI has also been the subject of a recent European Commission (EC) consultation document, to which CFA Institute submitted a response. This'training' involves using a large training data set that the computer algorithm can repeatedly go through (but typically with guidance and supervision) to learn through trial and error how to connect the input data (e.g., credit history, employment history, assets, purchasing history) with the desired output (e.g., the correct identification of a suitable risky portfolio). Although some attempts have been made to check the source code of algorithmic traders, the most effective protection against algorithmic errors are circuit breakers on markets that limit the amount of damage a failing algorithm can cause. Consider attending the CFA Institute European Investment Conference, held in Berlin this November.


Can Artificial Intelligence End the Human Race?

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The huge amount of financial data you create – credit card transactions, banking transactions, ATM withdrawals – all of these data are accessed and processed by computer algorithms. A researcher found that drivers with low-income and no drunken driving cases, were asked to pay a higher premium than high-income persons, but with drunken driving cases. Such data and models simply reflect the objective reality of the high degree of inequality that exist within society, and replicates that in the future through its predictions. The past data of people who have been "successful" – some specific output variables – are selected as indicators of success and correlated with various social and economic data of the candidate.


Machine learning is transforming lending

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In June 2015, the World Economic Forum published The Future of Financial Services - How disruptive innovations are reshaping the way financial services are structured, provisioned and consumed. The operational and statistical models are based on predictability of borrowers' behaviour across a large enough population set. The front-end is a platform that hosts one common online form which in turn becomes the source of several linkages to biometric identification databases, electronic KYC and FATF checks, autofill data from other platforms, credit bureau checks, National ID checks, social media scoring as well as a proprietary SME scoring module. He's a digital banking and digital banking financial services evangelist, practitioner, advisor and consultant.


Artificial Intelligence and the Threat to Humanity NewsClick

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The huge amount of financial data you create – credit card transactions, banking transactions, ATM withdrawals – all of these data are accessed and processed by computer algorithms. A researcher found that drivers with low-income and no drunken driving cases, were asked to pay a higher premium than high-income persons, but with drunken driving cases. Such data and models simply reflect the objective reality of the high degree of inequality that exist within society, and replicates that in the future through its predictions. The past data of people who have been "successful" – some specific output variables – are selected as indicators of success and correlated with various social and economic data of the candidate.


Machine Learning: Challenges and Opportunities in Credit Risk Modeling

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These additional dimensions typically include other financial information such as liquidity ratio, or behavioral information such as loan/trade credit payment behavior. Now let's look at three different machine learning algorithms: artificial neural networks, random forest, and boosting. We seek to answer the following questions: Do the machine learning models outperform the RiskCalc model's GAM framework in default prediction? The second dataset adds behavioral information, which includes credit line usage, loan payment behavior, and other loan type data.


How Deep Learning Machines Program Themselves – Saad Hussain – Medium

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Finally, once we understand how machines learn and what kind of skills they develop, we will learn how deep learning machines program themselves. Essentially, the kid has learnt'the relationship' between'the objective' (higher accuracy of shooting the ball through the hoop) and'the relevant parameters' (trajectory, use of force, distance etc.) Suppose, a machine has to learn to predict the risk of credit card default ("The Objective") given a large set of historical data on credit card defaults including demographic information, past payment details, credit limits etc. The machine will learn the relationship between the input parameters and the resulting credit default and develop a skill to predict future default by developing a complex mathematical model.


How Deep Learning Machines Program Themselves

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Finally, once we understand how machines learn and what kind of skills they develop, we will learn how deep learning machines program themselves. Essentially, the kid has learnt'the relationship' between'the objective' (higher accuracy of shooting the ball through the hoop) and'the relevant parameters' (trajectory, use of force, distance etc.) Suppose, a machine has to learn to predict the risk of credit card default ("The Objective") given a large set of historical data on credit card defaults including demographic information, past payment details, credit limits etc. The machine will learn the relationship between the input parameters and the resulting credit default and develop a skill to predict future default by developing a complex mathematical model.