Welcome to this project on predict whether a customer is eligible for Home loan or not in Apache Spark Machine Learning using Databricks platform community edition server which allows you to execute your spark code, free of cost on their server just by registering through email id. In this project, we explore Apache Spark and Machine Learning on the Databricks platform. I am a firm believer that the best way to learn is by doing. That's why I haven't included any purely theoretical lectures in this tutorial: you will learn everything on the way and be able to put it into practice straight away. Seeing the way each feature works will help you learn Apache Spark machine learning thoroughly by heart.

OppFi Inc., a 10-year-old fintech platform based in Chicago, targets U.S. households with an average of $50,000 in annual income that need extra cash for car repairs, medical bills, student loans and other expenses. Todd Schwartz, the company's chief executive, said its customers are employed and have bank accounts but are otherwise "locked out of mainstream financial services." The Morning Download delivers daily insights and news on business technology from the CIO Journal team. OppFi, which made its public-market debut last summer, uses an AI model, real-time data analytics and a proprietary scoring algorithm to automate the underwriting process. It generates a credit score by analyzing a loan applicant's online shopping habits, income and employment information, among other data sources.

Artificial Intelligence and its inherent bias seems to be an ongoing contributing factor in slowing minorities home loan approvals. An investigation by The Markup found lenders were more likely to deny home loans to people of color than to white people with similar financial characteristics. Specifically, 80% of Black applicants are more likely to be rejected, along with 40% of Latino applicants, and 70% of Native American applicants are likely to be denied. How detrimental is the secret bias hidden in mortgage algorithms? It's important to note that 45% of the country's largest mortgage lenders now offer online or app-based loan origination, as FinTech looks to play a major role in reducing bias in the home lending market, CultureBanx reported.

Artificial Intelligence and its inherent bias seems to be an ongoing contributing factor in slowing minorities home loan approvals. An investigation by The Markup found lenders were more likely to deny home loans to people of color than to white people with similar financial characteristics. Specifically, 80% of Black applicants are more likely to be rejected, along with 40% of Latino applicants, and 70% of Native American applicants are likely to be denied. How detrimental is the secret bias hidden in mortgage algorithms? It's important to note that 45% of the country's largest mortgage lenders now offer online or app-based loan origination, as FinTech looks to play a major role in reducing bias in the home lending market, CultureBanx reported.

Artificial Intelligence and its inherent bias may not be as judgmental as previously thought, at least in the case of home loans. It appears the use of algorithms for online mortgage lending can reduce discrimination against certain groups, including minorities, according to a recent study from the National Bureau of Economic Research. This could end up becoming the main tool in closing the racial wealth gap, especially as banks start using AI for lending decisions. The Breakdown You Need to Know: The study found that in person mortgage lenders typically reject minority applicants at a rate 6% higher than those with comparable economic backgrounds. However, when the application was online and involved an algorithm to make the decision, the acceptance and rejection rates were the same.

Robotic process automation (RPA) has been adopted across various industries to ease employee workloads while cutting costs – and banking is no exception. From taking over monotonous data-entry, to answering simple customer service queries, RPA has been able to save financial workers from spending time on repetitive, labor-intensive tasks. RPA combines robotic automation with artificial intelligence (AI) to automate human activities for banking, this could include data entry or basic customer service communication. RPA has revolutionized the banking industry by enabling banks to complete back-end tasks more accurately and efficiently without completely overhauling existing operating systems. Banks that utilize RPA have given employees back time to spend on more complex tasks while artificial intelligence technology handles back-end operations.

ONE THEORY HAS ARISEN in the decade since the subprime mortgage crisis: Machines may be better than humans at giving out home loans. A new Fannie Mae survey of mortgage lenders found that 40% of mortgage banks have deployed A.I.--using it to automate the document-heavy application process, detect fraud, and predict a borrower's likelihood of default. San Francisco–based Blend, for one, provides its online mortgage-application software to 114 lenders, including lending giant Wells Fargo, shaving at least a week off the approval process. Could it have prevented the mortgage meltdown? Maybe not entirely, but it might have lessened the severity as machines flagged warning signs sooner.

Chqbook is a Gurgaon based financial technology start-up that allows customers to explore, compare, book and get personal finance products such as home loans, personal loans, and credit cards. Chqbook is a marketplace for financial products -- that brings suppliers (banks and NBFC's), distributors, and customers onto a single platform -- both online and offline. The startup currently offers 23 options from the country's leading banks & NBFC's for home loans. It also offers customers a choice of 16 institutions for personal loans and has over 35 credit cards. Chqbook is currently operational in 14 cities for home loans and personal loans through its 400 plus verified experts on its platform.

Having the right skills are worth their weight in gold, especially when operating within the modern-day market that is known for its agility and operational precision. Perhaps, this is one of the main reasons why the industry is always on the lookout for professionals with dynamic skills, the ones who can reap higher customer satisfaction. But these prized skills, as is the case, are rarely available in the market. This makes the ones available an even more precious commodity. The challenge that every growing business faces today is to deliver an exceptional customer experience and at the same time utilize the skills of its human resources to its full capacity.

Even in the 1990s, when Australian banks copped a lot of flak for closing branches, consumer behaviour was changing. The banks may not have undertaken their branch network rationalisations in the most amenable fashion for the wider community but even then, as data from the Australian Prudential Regulation Authority show, actual points of representation didn't change as much as the headlines suggested. Indeed, points of representation actually increased for 11 straight years from 2001, the first year APRA started compiling proper data. The shift from traditional branch banking and physical currency has been immense. But the next generational shift in financial services – and services more generally – will be even more confronting.