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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.


Simplicity and transparency: the machine learning regulatory challenge ahead

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It uses machine learning algorithms to examine data relating to'rogue traders' and other financial services miscreants, to identify patterns of behaviour. Openness and transparency are likely to be vital in managing risk, because effective risk management requires identifying potential risks. Even standard credit risk assessment requires a human eye to check that the models make sense. It is vital that organisations have employees with the skills to understand and manage machine learning systems.


Is Machine Learning the best way to make the most in Finance? - Maruti Techlabs

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Machine Learning algorithms are able to provide accurate in-depth analysis of thousands of datasets. Once the data has been reduced to cluster levels we use the discriminant analysis, logistic regression and neural networks. Discriminant analysis and logistic regression are statistical techniques that take different approaches. Fraud detection process using machine learning starts with gathering and segmenting the data into three different segments.


Applications of Machine Learning in FinTech – Let's Talk Payments – Medium

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Companies in the lending Industry are using machine learning for predicting bad loans and for building credit risk models. ZestFinance: ZestFinance uses machine learning techniques and large-scale data analysis to consume vast amounts of data and make more accurate credit decisions. The company has expertise in big data mining, machine learning algorithms, security and consumer Web UX. BioCatch: BioCatch, is a leading provider of behavioral biometric, authentication and malware detection solutions for mobile and web applications.