Fred Rogers of Mister Rogers' Neighborhood shared that his mother would say, in times of crisis, "Look for the helpers. You will always find people who are helping." This advice holds true when looking at the potential for artificial intelligence to change the world. People are using technological advances to create and experiment with innovative approaches to global issues that have affected humanity for generations. The plight of displaced persons, the global food crisis and natural disasters are real challenges people face every day.
Mexican banks – already keen on digital solutions to optimize their processes and drive customer loyalty – are quickly seeing the benefits of artificial intelligence. Together with Brazil and the UK, Mexico is one of the countries where banks show most enthusiasm for using AI, according to a study published in June by GFT, an IT consultancy, which surveyed banks in eight countries. However, Mexico's banks are choosing the applications carefully. Many have rapidly taken to AI to power advanced chatbots. But for the moment, many prefer not to leave fraud or anti-money laundering detection in the control of such new technology.
We could initially model the problem as a machine learning or a deep learning problem. At this stage, we are concerned with the accuracy, choice and the efficiency of the model. Hence, the first quadrant is characterized by experimental analysis to prove value. We are also concerned with improving the existing KPIs. For example, if you are working with fraud detection or loan prediction – each of these applications has an existing KPI based on current techniques.
Companies are using AI to prevent and detect everything from routine employee theft to insider trading. Many banks and large corporations employ artificial intelligence to detect and prevent fraud and money laundering. Social media companies use machine learning to block illicit content such as child pornography. Businesses are constantly experimenting with new ways to use artificial intelligence for better risk management and faster, more responsive fraud detection -- and even to predict and prevent crimes. While today's basic technology is not necessarily revolutionary, the algorithms it uses and the results they can produce are.
Our initial results look promising, but there is room for improvement. We made a number of choices and assumptions for our initial analysis. Our next steps would be to go back and evaluate these to determine what changes we can make to tune our classifier. If we plan to use this classifier for a real-time credit card fraud detection system, we want to ensure that we can catch all the fraudulent transactions and also keep our customers happy by correctly identifying non-fraudulent transactions. Once we have a good classifier, we can use it directly with transactions arriving into Ignite in real-time. Additionally, with Ignite's continuous learning capabilities, we can refine and tune our classifier further with new data, as the data arrive. Finally, using Ignite as the basis for a real-time fraud detection system enables us to obtain many advantages, such as the ability to scale ML processing beyond a single node, the storage and manipulation of massive quantities of data, and zero ETL.
The Western Australia Police Force has said it will roll out body-worn cameras across the state from early 2019 as a way to improve evidence capture and offer greater safety for officers. Western Australia Police Commissioner Chris Dawson told The West Australian on Thursday that while members of the public are increasingly filming police incidents, equipping front-line officers remains a priority ahead of specific state government funding to help corroborate evidence. "Body-worn cameras are now commonly used in other policing jurisdictions, with potential benefits including improved evidence gathering and a greater opportunity to capture the whole of an incident rather than rely on piecemeal recordings," Dawson told the paper on Thursday. A WA Police spokesperson also said that they will be seeking technology that would automatically activate the cameras when officers would be about to use a weapon and back-capture at least 30 seconds of footage, according to the paper. The cameras will not record constantly during an officer's shift, it added, but will be required to when attending incidents such as family violence complaints.
This is the fifth in my series on five keys to using AI and machine learning in fraud detection. Fraudsters ensure that protecting customers' accounts is very complex and dynamic, a challenge where machine learning thrives. For continual performance improvement, fraud detection professionals should consider adaptive technologies designed to sharpen responses, particularly on marginal decisions. These are the transactions that are very close to the investigative triggers, either just above or just below the cutoff. It is on these margins where accuracy is most critical as there is a fine line between a false positive event -- a legitimate transaction which has scored too high, and a false negative event -- a fraudulent transaction which has scored too low.
Findings • No direct Missing Values • Zero NaN, missing values • For Features - Marriage, Education • Missing values are marked as 0 (zero) • Relation between • Bill amounts of previous month -- Pay amount of current month • If a previous month bill amount was high • Next month can be default 16. Feature Engineering 17. Imputation - MARRIAGE • MARRIAGE • 54 values are zero • We can impute this with mode 18. Imputation - EDUCATION • EDUCATION • 14 values are zero • We can impute this with mode 19. One Hot Encoding • MARRIAGE, SEX, and EDUCATION are numeric values but these are really categorical. If Bill Amount 0: Then convert Ratio to positive 2. Impute NaN to 1 (Higher the ratio, lesser the chance of Default) 26. Re-Sampling The target variable is not balanced.
After studying Machine learning for 4 months, it was time to practice what I had learned and hackathon was the best medium to test skills. We performed basic EDA on data to see how it looks. This hints that we can perform either oversampling or under sampling. We performed imputation on missing values by Mode since these were categorical values and most likely the mode value looked like a good option. Then we performed one hot encoding for categorical features.