Credit


AI may just create the illusion of good credit decisions

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AI has been conflated with big data, machine learning and neural networks. The reality is AI will make lending more consistent and efficient; however, it remains to be seen if it will make lending safer. But a handful of banks will basically be attracting and serving the same general demographic profiles and populations (think Wells Fargo and Bank of America) and therefore using standardized data sets to build their AI systems. For neural networks, it will be necessary to insert random mutations of the underlying data so as to allow the system to receive small tweaks which, over time, will make the algorithms stronger.


E-learning courses on Advanced Analytics, Credit Risk Modeling, and Fraud Analytics

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To be discussed is the use of descriptive analytics (using an unlabeled data set), predictive analytics (using a labeled data set) and social network learning (using a networked data set). He has done extensive research on big data& analytics, fraud detection, marketing analytics and credit risk management. Machine Learning, Management Science, IEEE Transactions on Neural Networks, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Evolutionary Computation, Journal of Machine Learning Research, …) and presented at international top conferences. He is author of the books Credit Risk Management: Basic Concepts, Analytics in a Big Data World, Fraud Analytics using Descriptive, Predictive and Social Network Techniques, and Credit Risk Analytics: Measurement Techniques, Applications, and Examples in SAS.


Credit Karma: Believe the hype for 'artificial narrow intelligence'

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By giving away free credit scores to more than 60 million people, Credit Karma upended the paid credit report market. And by offering free tax returns this year -- at the same time H&R Block and IBM Watson were bragging about AI-powered tax filings -- the company stormed the $8.9 billion tax preparation industry. Credit Karma works like this: Members supply personal information (name, address, phone, social security number) to receive credit scores and, if desired, to be matched with the best credit card, personal loan, and auto loan offers, and to file their federal income taxes -- all for free. And to do this, the service relies not on general artificial intelligence (AI) but on artificial narrow intelligence (ANI).


Where can Machine Learning be Applied to Improve Banking Performance? - Accenture

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Traditional fraud systems identify fraudulent transactions based on specified, non-personalised rules, such as if a customer spends money abroad. Machine Learning credit default prediction models allow for more accurate, instant credit decisions as they can automatically use a much broader range of data sources including news and business networks. This has the potential to transform back office risk and finance reporting processes, enabling banks to meet regulatory reporting requirements at speed, whilst reducing costs. From a performance perspective, Machine Learning algorithms autonomously evolve and search for new patterns in internal and external, quantitative and qualitative data, making real-time high-frequency trading decisions to exploit volatility in individual stocks.


What the Rise of Chat Bots Means for Financial Services

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Furthermore, with so many desirable benefits, the risks involved and how best to approach chat bots in the FS industry must be carefully considered! ClearScore have developed a credit checking service known as "Coaching" intended to help millions of people improve their credit score. My suggestion for those looking to enter the Financial Services chat bot market, would be to consider the risks associated. When it comes to finances, these chat bots shouldn't just avoid falling at the first hurdle, but should avoid falling at any hurdle!


Credit Scoring Models - Open Risk Manual

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The credit scoring model collection focuses on the classic one period credit assessment / classification problem that typically produces a credit score and/or a probabilistic estimate of credit risk on the basis of selected characteristics of a borrower. For example a logistic regression based credit score model applied to individuals might differ from one applied to SME in the number and type of characteristics used. In classic credit scoring the population characteristics are typically analyzed statistically but not are not modeled jointly with the outcome variable. Parametric models posit explicit functional relations between a finite number of variables versus non-parametric models which imply the functional form directly from the data, implicitly allowing an infinite number of variables.


How artificial intelligence is impacting the highest levels of finance

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The financial industry has very specific information requirements. According to the SEC, there are $3.7 trillion in municipal bonds issued by thousands of cities across the country.


Did Artificial Intelligence Deny You Credit?

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When a system makes decisions based on multiple factors it is important to identify which factors cause the decisions and their relative contribution. To measure the influence of race in a specific credit decision, we redo the application process, keeping the debt-to-income ratio the same but changing the race of the applicant. Say an applicant, Alice, with a credit score of 730 and no car or home loan, is denied credit. So far we have evaluated our methods on decision systems that we created by training common machine learning algorithms with real world data sets.


Did Artificial Intelligence Deny You Credit?

#artificialintelligence

When a system makes decisions based on multiple factors it is important to identify which factors cause the decisions and their relative contribution. To measure the influence of race in a specific credit decision, we redo the application process, keeping the debt-to-income ratio the same but changing the race of the applicant. Say an applicant, Alice, with a credit score of 730 and no car or home loan, is denied credit. So far we have evaluated our methods on decision systems that we created by training common machine learning algorithms with real world data sets.


Did artificial intelligence deny you credit?

#artificialintelligence

When a system makes decisions based on multiple factors it is important to identify which factors cause the decisions and their relative contribution. To measure the influence of race in a specific credit decision, we redo the application process, keeping the debt-to-income ratio the same but changing the race of the applicant. Say an applicant, Alice, with a credit score of 730 and no car or home loan, is denied credit. So far we have evaluated our methods on decision systems that we created by training common machine learning algorithms with real world data sets.