THE OTHER SIDE OF INVESTOR PSYCHOLOGY

SARAH BEHNKE | April 21, 2016

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I still sometimes find it hard to believe that I've been working in the financial services industry for more than 15 years. Perhaps part of that disbelief stems from the fact that I was an English major in college and never expected to land in this industry. At any rate, there’s no denying the fact that I'm an "insider" in this field. And since I've spent the majority of my 15-plus years working in the areas of communications and marketing, I'm also an old hand at crafting messages that serve to promote the importance of sound financial and retirement planning. I'm guessing you're familiar with those messages – after all, if you've discovered this article, you're probably actively seeking out resources to help you gain financial knowledge.

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OTHER ARTICLES

Machine Learning in Finance – Present and Future Applications

Article | March 4, 2020

Machine learning has had fruitful applications in finance well before the advent of mobile banking apps, proficient chatbots, or search engines. Given the high volume, accurate historical records, and quantitative nature of the finance world, few industries are better suited for artificial intelligence. There are more uses cases of machine learning in finance than ever before, a trend perpetuated by more accessible computing power and more accessible machine learning tools (such as Google’s Tensorflow).

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BIG TECH IN FINANCE: A DEEP DIVE INTO THE FUTURE OF FINTECH

Article | February 10, 2020

The following article looks at Big Tech and its impact on the financial services sector. Whilst competition from small fintech startups will certainly take away some market share from traditional banks, the impact of “GAFA” could be huge. The fintech movement did more than unbundle banking and its core services — it spurred financial inclusion across Asia, increased overall economic growth, and made significant inroads into the finance value chain. The born-digital companies brought technology to the forefront, attacking the traditional risk-averse sector from various points — digital payments, insurance, P2P lending, and investment management, among other avenues.

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How Mentorship Makes all the Difference for Next-Gen Advisors

Article | July 1, 2020

According to a recent CNBC study, only half of American workers report having a mentor, and those who do enjoy greater job satisfaction than their peers who are doing it alone. In fact, 91% of U.S. employees who have mentors say they are satisfied with their jobs. Mentorship is especially important for young financial advisors who are just starting out, trying to get a foothold into the world of wealth management. There’s a lot to learn, and not a lot of room for trial and error. As a result, many would-be advisors give up when things get tough. That’s where mentorship can really make a difference.

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Five Application Scenarios of AI in Banking

Article | April 13, 2021

Over the past decades, banks have been improving their ways of interacting with customers. They have tailored modern technology to the specific character of their work. For example, in the 1960s, the first ATMs appeared, and ten years later, there were already cards for payment. At the beginning of our century, users learned about round-the-clock online banking, and in 2010, they heard about mobile banking. But the development of the financial system didn’t stop there, as the digital age is opening up new opportunities — the use of Artificial Intelligence. By 2023, banks are projected to save $447 billion by applying AI apps. We will tell you how financial institutions are making use of this technology in their operations today. AI-powered chatbots Chatbots are AI-enabled conversational interfaces. This is one of the most popular cases of applying AI in banking. Bots communicate with thousands of customers on behalf of the bank without requiring large expenses. Researchers have estimated that financial institutions save four minutes for each communication that the chatbot handles. Since customers use mobile apps to carry out monetary transactions, banks embed chatbot services in them. This makes it possible to attract users’ attention and create a brand that is recognizable in the market. For example, Bank of America launched a chatbot that sends users notifications, informs them about their balances, makes recommendations for saving money, provides updates to credit reports, and so on. This is the way the bank helps its clients to make informed decisions. Another example is the launch of the Ceba chatbot, which brought great success to the Australian Commonwealth Bank. With its help, about half a million customers were able to solve more than two hundred banking issues: activate their cards, check account balances, withdraw cash, etc. Mobile banking AI functionality in mobile apps is becoming more proactive, personalized, and advanced. For example, Royal Bank of Canada has included Siri in its iOS app. Now, to send money to another card, it’s enough to say something like: "Hey, Siri, send $30 to Lisa!" - and confirm the transaction using Touch ID. Thanks to AI, banks generate 66% more revenue from mobile banking users than when customers visit branches. Banking organizations are paying close attention to this technology to improve their quality of services and remain competitive in the market. Data collection and analysis Banking institutions record millions of business transactions every day. The volume of information generated by banks is enormous, so its collection and registration turn into an overwhelming task for employees. Structuring and recording this data is impossible until there is a plan for its use. Therefore, determining the relationship between the collected data is challenging, especially when a bank has thousands of clients. There used to be the following approach: a client came to a meeting with a bank employee who knew their name and financial history and understood what options were better to offer. But that's history now. With the wealth of data coming from countless transactions, banks are trying to implement innovative business ideas and risk management solutions. AI-based apps collect and analyze data. This improves the user experience. The information can be used for granting loans or detecting fraud. Companies that estimated their profit from Big Data analysis have reported an average increase in revenue by 8% and a reduction in costs by 10%. Risk management Extension of credit is quite a challenging task for bankers. If a bank gives money to insolvent customers, it can get into difficulties. If a borrower loses a stable income, this leads to default. According to statistics, in 2020, credit card delinquencies in the U.S. rose by 1.4% within six months. AI-powered systems can appraise customer credit histories more accurately to avoid this level of default. Mobile banking apps track financial transactions and analyze user data. This helps banks anticipate the risks associated with issuing loans, such as customer insolvency or the threat of fraud. Data security According to the Federal Trade Commission report for 2020, credit card fraud is the most common type of personal data theft. AI-based systems are effective against malefactors. The programs analyze customer behavior, location, and financial habits and trigger a security mechanism if they detect any unusual activity. ABI Research estimates that spending on AI and cybersecurity analytics will amount to $96 billion by the end of 2021. Amazon has already acquired harvest.AI - an AI cyber security startup - and launched Macie - a service that applies Machine Learning to detect, sort, and structure data in S3 cloud storage.

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We're the UK's largest provider of tenant referencing, rent guarantee and specialist property insurance to the lettings industry. We offer more than just tenant references, our aim is to help letting agents add value to their business. That's why over 2,000 great letting agents use HomeLet - from small family run offices to some of the best known letting agent brands in the UK...

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