European Union Finances 2015: statement on the 2015 EU Budget and measures to counter fraud and financial mismanagement

| June 3, 2016

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In 1980, following a recommendation by the Public Accounts Committee (PAC), the government agreed to present an annual statement (statement) to Parliament giving details of the Budget of the European Union…

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WHY FINANCIAL INSTITUTIONS ARE ON CLOUD NINE POST-PANDEMIC

Article | June 4, 2021

Financial institutions have, for a while now, been operating in a highly cost challenged environment. These firms will continue walking the tight rope of executing on efficiency, digital transformation and supporting the business. Post-COVID when our dear planet begins to get back to some form of normality in the months ahead, it does not necessarily assume that wallets will be loser and further budget constraints are expected to be with us for some time. As we know the Genie is out of the bottle on the whole “agile” working theory and the Cloud providers have responded in kind such as providing virtual desktops and VPN solutions. Of course not forgetting the Video calling enablement which has coined a phrase never to leave our vocabulary “sorry I was on mute”. Cost pressures aside businesses are already reassessing the effectiveness of their technology stacks. I believe we will see an acceleration of an already giddy pace by firms to move parts of their estate and applications to the public cloud. It is not only essential from a practical basis covering the usual themes of cost, storage planning on demand compute etc but if you want to retain the best talent in technology you need to be exposing them to the likes of AWS, GCP and Azure in some form. Data is the new oil As to my world in data various analogies “data is the new oil” etc, but getting beyond the taglines the public cloud is shaking up the status quo. From off-the-shelf Amazon style access to data products via a web store or to throw in another term “supermarket”. Fundamentally the barrier to entry for clients to access data, storage and enormous compute resource is really down to what you can afford. Efficiencies on compute, serverless technologies pay for what you use not pay for standby is changing the paradigm in architecture. Thereby pushing boundaries in innovation, experimentation and exposing teams to AI/ML as a utility as opposed to things you read about in journals or online. No two businesses are the same which is why certain firms are further in the journey than others. But regardless of the path financial institutions decide to go down, it does not change the fact that data needs to be delivered to the right place, at the right time, and in a preferred format. Some firms will simply want their channel partners to ship data into the cloud as an end point. From Satellites to the Cloud This leads me into my next comparison. I was lucky enough (or unlucky) to be there when the internet created another paradigm shift as a delivery end point for data. Prior to that I spent many years plugging firms into Satellites or Leased lines for the delivery of Market Data. As a younger man I thought those days would never end! If the internet became the end point that people used to get data into their own network, then the cloud to a certain extent is the modern day equivalent. After all, if firms want to use cloud as an end point into a physical data centre or on-premise, they can do that. Alternatively, if the firm wants to use the data exclusively within the cloud, then that is also achievable in this day and age.

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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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How Fintech Startups Are Disrupting the Payments Industry

Article | February 21, 2020

Long in the past, transfers of value took place between royalty, merchants and commoners who all used gold, silver, cattle and other physical commodities to thrive and survive. That ended in 1971 when the U.S. dollar and other world fiat systems fully detached from the gold standard and embraced floating exchange rates. Over the past 50 years, financial institutions built payment systems that are partially obsolescing in the wake of fintech disruptions like virtual currencies, distributed ledgers and decentralized protocols.

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Is This A Fintech Bubble?

Article | February 24, 2020

Before I begin…good news for people nervous about the stock market panic today…. CNBC is airing a ‘Markets in Turmoil’ tonight. It never has failed at producing an investable bottom. If you are confused or angry…you might be too heavily weighted in stocks. The warning signs have been epic. Read Ben Carlson’s excellent piece titled ‘markets have always been rigged, broken and manipulated ‘.

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Harden promises a risk management, insurance and employee benefit plan based on insight of your business; insight born from a unique discovery process implemented by experienced consultants who work with you side by side. You are a unique, individual company...

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