The Difference Between Mortgage Brokers and Loan Officers

| October 4, 2017

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If you’re in the process of finding a new mortgage company whether you’re looking to refinance or buy a new home you may come across two terms that seem interchangeable but actually aren’t. The terms, mortgage broker and loan officer, both refer to individuals who work in the mortgage industry. However, there are some differences between them.

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How to Fight Fraud and Financial Crime Using Machine Learning Applications

Article | April 20, 2020

Going far away beyond conventional attack detection, advanced machine learning operations assist organizations to stay one step ahead of financial fraudsters. We hear tons of stories about account takeovers and hacking also. How can financial institutions detect and mitigate these attacks? The world of fraud prevention in banking institutions has always been supported by rules. Bankers and their engineers were uniting rules engines on the banking data system to stop or identify common fraud patterns. For quite a while, this was sufficient. But today we are experiencing a change of society, a digital and technological revolution. Following the primary iPhone, and therefore the later mobile internet explosion, people are interconnected all the time, everywhere and for all quite useful. In this digital age, the digitization of means and behaviors forces corporations to revise their business model. As a result, banking institutions are going massively online and digital-first. Both the bank users and customers have unfolded their behaviors with the brand-new means offered by the digital era. Learn more: https://deck7.io/Women-Leadership-verrency-audrey-blackmon With the shift towards universal digitalization, perpetrators are finding new weak spots in financial digital applications. Ironically, the technology works both ways: it accommodates firms to supply more reliable customer experience and optimize operations and, at an equivalent time, aids cybercriminals in performing numerous sophisticated unlawful schemes. According to the Association of Certified Fraud Examiners (ACFE), 30pc of fraud occurrences happened in small businesses, and 60pc of small-business fraud victims did not retrieve any of their losses. According to Statista, in 2017, the global FDP (fraud detection and prevention) market was calculated to be worth $16.6 billion. According to McKinsey, worldwide losses from card fraud could be close to $44 billion by 2025. Financial crimes do not limit crimes like credit card fraud, tax dodging, and elder abuse. In fact, it includes much broader offenses – such as Identity theft, human trafficking, phishing, pharming, drug trafficking, money laundering, and terrorist financing that can have enduring impacts on society. Fighting financial fraud is difficult because fraudsters frequently change and adapt. The moment you figure out how to identify and prevent one scam, a unique one emerges to take its spot. Identifying, eliminating, and blocking these threats are sensitive points for e-commerce and banking industries. Sincerely, the best technology for combating fraud is one that can evolve and adapt as instantly as the fraudster’s tactics. That’s what makes machine learning (ML) systems ideal to fight fraud and financial crime. The big problem is that companies think they need to establish rules, policies, and procedures to prevent fraud. But today’s criminals are much more sophisticated and are able to circumvent these business rules. Businesses need to take a more dynamic approach that includes business rules as well as machine learning and AI to learn from evolving criminal behavior and deliver a more sophisticated and effective approach to dealing with financial crimes. Andrew Simpson, Chief Operating Officer of CaseWare Analytics. Why use machine learning to combat financial fraud? Machine Learning knocks down the conventional ways of detecting fraud. It’s quicker, works with extensive amounts of data, and doesn’t rely on human resources. When designed optimally, it absorbs, adapts, and uncovers emerging patterns without the over-adaptation resulting in too many false positives. It’s time for ML to conclusively take center stage in assisting firms to recognize and counter fraud as fast as it’s performed. How Machine Learning Helps in Fighting Fraud and Financial Crime? Machine learning can learn normal behavior from training data and recognize abnormal behaviors that indicates money laundering, like, when money is transferred between suspicious geographies, active movement of funds between different accounts, or invoicing number sequences have been falsified. Machine learning is continually learning, and so they can recognize when the pattern of laundering change and adjust rapidly. Analyzing Huge Amounts of Transaction Data One of the most powerful features of machine learning algorithms is that they can analyze huge numbers of transaction data and flag suspicious transactions with highly accurate risk scores in real-time. Its algorithms serve 24/7 and process an immense amount of information with the flip of a switch. This risky analytics method recognizes complex patterns that are challenging for analysts to identify; this means banks and financial organizations are far more operationally proficient while detecting more fraud. The algorithms take various factors into account, including; customer’s location, the device used, and other circumstantial data points to form a detailed picture of every transaction. This strategy improves real-time decisions and protects customers against fraud, all without affecting the user experience. Thanks to extensive technological development, organizations will frequently rely on machine learning algorithms to determine which transactions are suspicious. Learn more: https://www.sas.com/en_in/insights/articles/risk-fraud/strategies-fraud-detection.html#/ Supervised and Unsupervised Learning for Detecting Complex Patterns Machines can be programmed to self-learn in an unsupervised model with ML so that transactions that do not conform to a set pattern are recognized and hence can be actioned upon in right period. Machine Learning automatizes the extraction of aware and unaware patterns from data. Once it identifies those patterns, it can employ what it learns to new and unseen data. The machine learns and modifies as new outcomes and new patterns are introduced to it via a feedback loop. In fraud detection, supervised machine learning algorithms can self-learn from targets within the data. While training a supervised model, it's important to present to it both fraudulent and non-fraudulent records that have been labeled as such. Unsupervised Machine Learning is different. It reveals potentially unusual risks you might not watch for because it works without a target. Instead, it looks for irregularities in the data. Machine Learning in Fraud Detection The fraud detection method employing machine learning starts with gathering and segmenting the data. Alongside this, the machine learning model receives training sets that train it to predict the possibility of fraud. Conclusively, it creates a fraud detection model: Input data- The first step is data input, which differs in Machine learning and humans. Humans strive to comprehend massive amounts of data, such a task is a five-finger play for ML. The more data an ML model eats, the better it can learn and polish its fraud detection abilities. Extract Features- Extracted features defining good customer behavior and deceitful behavior are added. These features normally include the customer’s location, identity, orders, network, and preferred payment method. Based on the complexity of the fraud detection system, the list of examined features can vary. Train Algorithm- Further in this process, a training algorithm is launched. In short, this algorithm is a collection of rules that a machine learning model has to pursue when deciding whether an operation is genuine or fraudulent. The more data a business can supply for a training set, the more reliable the ML model will be. Create Model- After the training is over, an organization receives a fraud detection model acceptable for their business. This model can detect fraud in no time with great accuracy. To be efficient in credit card fraud detection, a machine learning model needs to be continually improved and updated. Eventually, fraudsters will turn up with new bamboozle to game the system unless you keep it updated. Employing advanced fraud protection and detection systems electrified by ML, multiple industries can keep their finances secured. Capgemini alleges their ML fraud detection system can lessen fraud investigation time by 70% while boosting accuracy by 90%. Another ML fraud prevention solution provider, Feedzai, alleges that a well-trained machine learning solution can recognize and prevent 95% of all fraud while reducing the amount of human labor needed during the investigation stage. Reduction of False Positives With the level of complicatedness in today’s financial infrastructures, the term ‘false positive’ has become nearly correlated with the industry’s efforts to fight fraud. One of the banking’s most significant challenges is to minimize the number of false positives being generated, thereby saving time, money, and bypassing needlessly frustrating customers. AI and machine learning play a significant role in this area. Because they are proficient in examining a much more comprehensive set of data points, connections between entities and fraud patterns – including fraud scenarios not yet known to fraud analysts – the predominance of false positives can be extremely reduced. Bringing it all Together Multinationals like Airbnb, Yelp, and Jet.com are already employing AI solutions to get insights from big data and counter issues such as fake accounts, account takeover, payment fraud, and promotion abuse. Machine learning entertains all the messy work of data analysis and predictive analytics and empowers companies to grow and develop secure from financial fraud and crime. As mentioned, machine learning can be very convenient when it comes to fighting cybercrimes. ML prevents critical attacks on users’ and companies’ finances. It’s a quick, up-to-date, and cost-effective method to shield customers and the company’s data.

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Putting humans at the helm of finance innovation

Article | February 28, 2020

As technologies such as 5G, IoT and AI are rolled out across industries, old business models are being overturned and new ones created, all in the name of progress. Even the most established industries run the risk of being significantly weakened, or even made redundant – so organizations will have to embrace change to survive. Business agility is crucial to responding to market changes, challenges and opportunities. Embracing the latest technologies, and fast, seems to be the order of the day. But to ensure this can be delivered effectively, a new generation of enterprise resource planning (ERP) systems – powered by artificial intelligence (AI) – are making an entrance.

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Insights from the Ground: Managing Through the COVID-19 Pandemic

Article | July 16, 2020

In this series, we share insights from our Citi Country Officers (CCOs) around the globe as they reflect on their experiences during COVID-19. CCOs are responsible for leading the entire Citi franchise in their country. They provide alignment and leadership to bring our global strategy to life in each of their jurisdictions. For us at Citi in Italy we prepared for the COVID pandemic in a number of different ways. First, we leveraged lessons learned from our colleagues in the countries that were hit before us, including China and Korea, even if their context was quite different from the European one.

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Applied Data Science in Fintech

Article | February 28, 2020

Under the supervision of Dr. Mario Gillrich and Maria Pelli, this course explores how new entrants in banking and finance are leveraging new technologies and methodologies to help traditional banks, their corporate clients and consumers use data and algorithms to better manage their financial operations and processes.

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Spotlight

Fino Payments Bank Ltd

Fino Payments Bank comes to you from an institution that has served the country’s banking needs for over a decade. We truly value your hard work and understand how important it is to you and your family. It is therefore, our continuous endeavor to provide you with an unmatched banking experience, through our simple products & services that can easily be accessed anytime, anywhere, and in your neighbourhood too.

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