Artificial intelligence has given the world of banking and the financial industry as a whole a way to meet the demands of customers who want smarter, more convenient, safer ways to access, spend, save and invest their money. We've put together a rundown of how AI is being used in finance and the companies leading the way. A recent study found 77% of consumers preferred paying with a debit or credit card compared to only 12% who favored cash. But easier payment options isn't the only reason the availability of credit is important to consumers. Having good credit aids in receiving favorable financing options, landing jobs and renting an apartment, to name a few examples.
An independent expert group tasked with advising the European Commission to inform its regulatory response to artificial intelligence -- to underpin EU lawmakers' stated aim of ensuring AI developments are "human centric" -- has published its policy and investment recommendations. This follows earlier ethics guidelines for "trustworthy AI", put out by the High Level Expert Group (HLEG) for AI back in April, when the Commission also called for participants to test the draft rules. The AI HLEG's full policy recommendations comprise a highly detailed 50-page document -- which can be downloaded from this web page. The group, which was set up in June 2018, is made up of a mix of industry AI experts, civic society representatives, political advisers and policy wonks, academics and legal experts. The document includes warnings on the use of AI for mass surveillance and scoring of EU citizens, such as China's social credit system, with the group calling for an outright ban on "AI-enabled mass scale scoring of individuals".
Have you ever had a legitimate credit card transaction declined on the Web or your smartphone? It's a real hassle, to be sure. But now, thanks to a new payment protocol being introduced by Mastercard that relies on deep learning to authenticate purchasers, you should see fewer false positives, less fraud, and faster approvals to boot. Billions of dollars are on the line in the battle for control of the nation's electronic payment byways. Fraudsters are eager to exploit any technological weakness to steal others' hard-earned money, while banks, retailers, and consumers just want to make transactions as simple and secure as possible.
Line Corp. has unveiled new services, from credit scoring to an AI-powered robot receptionist, as the operator of Japan's dominant messaging platform seeks to expand beyond chat. Line Score will rate users based on information they provide as well as their interactions with other services on the platform. That will determine interest rates and credit limits for a loan service set to be available this summer, executives said Thursday. The company is trying to move away from an advertising-reliant business model as its user growth plateaus, doubling down on financial services powered by artificial intelligence. Last year the firm raised ¥148 billion ($1.4 billion) in a convertible bond sale to help fund that expansion.
Line Corp. unveiled new services from credit scoring to an AI-powered robot receptionist, as the operator of Japan's dominant messaging platform seeks to expand beyond chat. Line Score will rate users based on information they provide as well as their interaction with other services on the platform. That will determine interest rates and credit limits for a loan service available this summer, executives said Thursday. The company is trying to move away from an advertising-reliant business model as its user growth plateaus, doubling down on financial services powered by artificial intelligence. The company last year raised ¥148 billion ($1.4 billion) in a convertible bond sale to help fund that expansion.
I have submitted my own project using a dataset of my choosing. My project has been reviewed both by my peers and the professor. I chose to work with Credit Card Fraud Detection, It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. The datasets contains transactions made by credit cards in September 2013 by european cardholders. Due to imbalancing nature of the data, many observations could be predicted as False Negative, in this case Legal Transactions instead of Fraudolent Transaction.
Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However purchase behaviour and fraudster strategies may change over time. This phenomenon is named dataset shift or concept drift in the domain of fraud detection. In this paper, we present a method to quantify day-by-day the dataset shift in our face-to-face credit card transactions dataset (card holder located in the shop) . In practice, we classify the days against each other and measure the efficiency of the classification. The more efficient the classification, the more different the buying behaviour between two days, and vice versa. Therefore, we obtain a distance matrix characterizing the dataset shift. After an agglomerative clustering of the distance matrix, we observe that the dataset shift pattern matches the calendar events for this time period (holidays, week-ends, etc). We then incorporate this dataset shift knowledge in the credit card fraud detection task as a new feature. This leads to a small improvement of the detection.
Credit risk modelling is an integral part of the global financial system. While there has been great attention paid to neural network models for credit default prediction, such models often lack the required interpretation mechanisms and measures of the uncertainty around their predictions. This work develops and compares Bayesian Neural Networks(BNNs) for credit card default modelling. This includes a BNNs trained by Gaussian approximation and the first implementation of BNNs trained by Hybrid Monte Carlo(HMC) in credit risk modelling. The results on the Taiwan Credit Dataset show that BNNs with Automatic Relevance Determination(ARD) outperform normal BNNs without ARD. The results also show that BNNs trained by Gaussian approximation display similar predictive performance to those trained by the HMC. The results further show that BNN with ARD can be used to draw inferences about the relative importance of different features thus critically aiding decision makers in explaining model output to consumers. The robustness of this result is reinforced by high levels of congruence between the features identified as important using the two different approaches for training BNNs.
Diego Caicedo is the Co-Founder and CEO of OmniBnk, a neobank that provides financial services to small businesses in Latin America. Artificial intelligence and machine learning are said to revolutionize the financial world, changing the banking experience for the better. The implications of the technology are vast, though most banks are still in the early stages of adopting AI technologies. A survey by Narrative Science and the National Business Research Institute found that 32% of financial services executives confirmed that they are already using AI technologies such as predictive analytics, recommendation engines, and voice recognition. One major hindrance to AI adoption is legacy systems.
As technology opens the doors to vast troves of data, opportunities are emerging to create new insights on a small business's health and prospects. Insights from this data have the potential to resolve two defining issues that have faced lenders and borrowers in the sector: heterogeneity--the fact that all small businesses are different, making it difficult to extrapolate from one example to the next--and information opacity, the fact that it is hard to know what is really going on inside a small business. And what if technology had the power to make a small business owner significantly wiser about their cash flow, and a lender wiser as well? What if new loan products and services made it easier to quickly and accurately predict the creditworthiness of a small business, much like a consumer's personal credit score helps banks predict creditworthiness for personal loans, credit cards, and mortgages? What if a small business owner had a dashboard of their business activities, including cash projections and insights on sales and cost trends that helped them weave an end-to-end picture of their business's financial health?