Connected Finances: Top Benefits of Using IoT Technology in Banking

DENIS NOVIKOV | February 19, 2020

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Technology has always been the main force behind changes in the banking sector, and now the Internet of Things is going to change the nature of banking itself. If we step back from such isolated technologies like blockchain or the smartphone and try to see the global picture, we easily notice that something is changing, and that’s the way of global digitalization in the financial sector.

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FIDELIS CM is an FX brokerage founded by leading industry experts with a clear understanding of what each trader needs. Our modus operand centers on client satisfaction, offering the best FX trading products as well as conditions all within a pure STP flawless trading environment. We collaborate with excellent tier 1 banks to provide our valued clients with deep bank liquidity and tight spreads; our NDD model offers a fast execution with no re-quotes. We continuously strive to provide the latest technology, offering a range of trading platforms. Our core values are trust, transparency and reliability; our Mission is to provide an optimum solution for all FX enthusiasts, from retail traders to Money Managers.

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Cybersecurity: The Hidden Risks of Fintech Services

Article | March 20, 2020

Fintech has drastically improved the products and the services of the traditional financial services in the past few years. However, even after many financial institutions have readily adopted fintech services, there are still some hidden risks in the aforementioned industry. For instance, the integration of the fintech services in the existing banking solutions raised a severe concern for data security. Also, the rapid growth of digital platforms made the fintech industry and its customers uniquely vulnerable to various breaches in IT security networks.

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7 FinTech Challenges and Ways to Overcome Them

Article | March 20, 2020

2020 has been a challenging year for all industries. As COVID-19 continues to create uncertainty, many FinTech brands are under stress for a number of reasons. However, FinTech brands can use this opportunity to build their reputation and emerge as a substantial entity after the crisis has passed. Many FinTech organizations are putting in their thoughts on big ideas and innovative digital offerings that meet customer demand for a frictionless and seamless banking experience. This article aims to list down the challenges faced by FinTech brands and effective ways to resolve them: Challenges Faced by FinTech Brands in Generating Demand In this unprecedented time, lead generation is on high priority to acquiring new customers. The pressure is to get on board with working remotely, adapting to new challenges, changes, and dealing with customers having urgent & new requirements. FinTech companies have faced unique challenges over the past year. They are using complex technologies to develop better products & services for businesses. Replacing traditional methods to improve financial services need strategic planning, technological advancement, and original content marketing ideas to survive in the digital age. Read on to find out the challenges faced by FinTech brands in generating demand for their solutions. Impact of Covid-19 COVID-19 continues to create uncertainty due to the widespread lockdown. Many FinTech brands are under the stress of counting recent losses, cost-effectiveness, and rethinking their offerings to adapt them to changing needs. The banking & finance industries are now shifting from response to recovery. They are now investing in introducing new FinTech apps for the post-pandemic world. According to a recent survey, almost 82% of the citizens don't want to visit their banks and try digital apps to carry out financial transactions. FinTech brands are grabbing this opportunity to reach out to a new demographics by testing and adding new product range. Today, popular FinTech apps include mobile banking, e-wallets, contactless payments, international money transfer, retail banking, stock trading, FinTech loan app, InsurTech, etc. Go mobile Go digital. As a FinTech brand develops innovative solutions to be used during the pandemic, they also need to figure out ways to promote these products and services to reach their target audience. You are getting acquainted with various social media platforms, and understanding your target audience will help you reach wider. Identifying the top social media platform that works best for your product and service through content marketing will boost your customer base, reduce churn, and attract potential customers. Other functional content marketing solutions that you can think of includes web – content syndication, social media, mobile app – advertisements, and brand awareness content. Inefficiency to maintain a healthy lead pipeline Several FinTech brands are reassessing their approach, their budget, goals, and their offering. Those quickest to adapt to this change will lead the market and continue to grow. According to a research report, B2B specialists make 48% purchases online, up from 38% before the COVID-19 outbreak; this trend is likely to increase. FinTech companies need to generate more new leads than ever to maintain a healthy lead pipeline. To do so, they need to be where their audience is. • Finding the best social media platform for your industry • Rethinking events – Online & Offline • Exploring online opportunities – Webinars/Podcasts/Live Streaming/ Live Q&A/Online Sessions/ Live Feeds • Publish demo videos Building trust One of the most significant issues and struggles with a FinTech brand is to gain the trust of their consumers. Consumers usually select financial service providers as they are trusted by their families for generations. These financial brands should be able to handle issues such as security, confidentiality, and digital fraud. The brand should also comply with the latest financial regulations that need to be communicated. Social media is an ideal platform for brands to connect with their existing and potential customers. To build trust & loyalty between the company and the end-user, brands must focus on helping their customers rather than selling. Inadequate tech stacks to work remotely The inability to work remotely gave rise to new entrepreneurs with knowledge of finance who developed innovative solutions to help FinTech brands connect remotely. Another essential aspect that has evolved this industry is remodeling the user interface and customer experience. FinTech brands are largely coming out with innovative solutions that can help with: - Financial close - More visibility and transparency to financial transactions - Centralized data - Cloud support Cybersecurity Cybersecurity is a common problem across industries. With the advent of advanced technologies rises the need to develop stronger security. Considering the threat posed by cybercriminals and fraudsters, the financial system needs to handle this risk smartly. All the financial information remains sensitive, whether it's your social security number, card number, PINs, or password. With the growing number of smartphone users, FinTech becomes cheaper and easy to use. The process and services that were once monopolized by the banking sector are now available for all, helping develop innovative solutions, lower operating costs, and improve financial organizations' efficiency. Reaching Millennials FinTech brands should target millennials. They are fueling the market among money transfer applications and personal investment applications. Financial brands need to focus on financial management, lending, financing, and insurance applications. According to a report, 33% of millennials believe they won't need a bank at all in 5 years. Expanding FinTech press/media The increasing use of financial technology has given rise to a number of media houses covering them. Over the year, dozens of FinTech focused media sites, podcasts, and newsletters have been launched. Several authoritative publications have hired beat writers to pump up stories on trending topics and subject interest. Final Thoughts The FinTech market is growing in numbers, and the industry is heading towards the trillion-dollar industry. To grow as a FinTech brand, you need to ensure that you are better than your competitors. These challenges are temporary and can be overcome through practical approaches and technological advancements. While the challenges in deploying a FinTech firm continues, we have given a clearer perspective on how to overcome them in this blog. Overall, digital transformation for customer satisfaction is what is necessary. FAQs Q1. What risks are associated with FinTech products and/or services? A1. Cybersecurity, consumer data privacy & security, consumer data rights, online frauds and scams; cross-border transactions, anti-money laundering and countering terrorist financing; and digital identity risk are the key factors in the FinTech market. Q2. What are the benefits of FinTech? A2. FinTech has helped us drive positive change in the traditional financial services and foster innovation by creating products and service that benefits customers and small and big enterprises. Some of the benefits of FinTech products and services include convenience, digital resolutions, hassle-free practices, flexibility, high rate of approval, upgrade payment systems, customer services, and revenue, user-centric, transparency, and many more. Q3. What are the challenges for the financial services industry? A3. As mentioned in the blog, there are 7 key challenges faced by the FinTech Market they are: Pandemic situation Inefficiency to maintain a healthy lead pipeline Building trust Inadequate tech stacks to work remotely Cybersecurity Reaching Millennials Need to expand FinTech press/media { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "What risks are associated with FinTech products and/or services?", "acceptedAnswer": { "@type": "Answer", "text": "Cybersecurity, consumer data privacy & security, consumer data rights, online frauds and scams; cross-border transactions, anti-money laundering and countering terrorist financing; and digital identity risk are the key factors in the FinTech market." } },{ "@type": "Question", "name": "What are the benefits of FinTech?", "acceptedAnswer": { "@type": "Answer", "text": "FinTech has helped us drive positive change in the traditional financial services and foster innovation by creating products and service that benefits customers and small and big enterprises. Some of the benefits of FinTech products and services include convenience, digital resolutions, hassle-free practices, flexibility, high rate of approval, upgrade payment systems, customer services, and revenue, user-centric, transparency, and many more." } },{ "@type": "Question", "name": "What are the challenges for the financial services industry?", "acceptedAnswer": { "@type": "Answer", "text": "As mentioned in the blog, there are 7 key challenges faced by the FinTech Market they are: Pandemic situation Inefficiency to maintain a healthy lead pipeline Building trust Inadequate tech stacks to work remotely Cybersecurity Reaching Millennials Need to expand FinTech press/media" } }] }

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4 Fintech Trends to Watch For in 2020

Article | March 20, 2020

The fintech industry is one of the most visibly disruptive sectors since it can dramatically impact everyday consumers as well as the business of all sizes. It’s also potentially a highly regulated sector, with governments and regulators well aware of the need to both protect consumers and businesses, and to provide a fair, competitive environment for industry players.

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How to Fight Fraud and Financial Crime Using Machine Learning Applications

Article | March 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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Spotlight

Fidelis Capital Markets

FIDELIS CM is an FX brokerage founded by leading industry experts with a clear understanding of what each trader needs. Our modus operand centers on client satisfaction, offering the best FX trading products as well as conditions all within a pure STP flawless trading environment. We collaborate with excellent tier 1 banks to provide our valued clients with deep bank liquidity and tight spreads; our NDD model offers a fast execution with no re-quotes. We continuously strive to provide the latest technology, offering a range of trading platforms. Our core values are trust, transparency and reliability; our Mission is to provide an optimum solution for all FX enthusiasts, from retail traders to Money Managers.

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