It's clear that AI is transforming the way banks operate, but what's not always clear is how they can successfully implement and deploy AI projects. Experts recommend a shared, centralized infrastructure for AI--a full-stack solution that includes both hardware and software. This approach, known as AI as a platform, is ideal for three reasons. One, it consolidates expertise, productivity, and scale. And three, it drives down total cost of ownership with an efficient utilization of compute and storage resources.

Artificial intelligence has offered a plethora of benefits for businesses in every sector. The ecommerce industry is among those most benefiting from advances in AI. Therefore, it is no surprise that the market for AI-enabled ecommerce services is projected to be worth nearly $17 billion by 2030. Ecommerce giants like Amazon are finding creative ways to leverage AI. In 2018, Blake Morgan wrote an article in Forbes detailing how Amazon rebranded itself around AI. AI technology helped the online titan improve product forecasting, deliver a higher ROI on ads to sellers and make better product recommendations.

If you received this in your inbox, thank you for signing up and your vote of confidence. If you're reading this as a post on our site, sign up here so you can receive it directly in the future. Every week, I'll take a look at the hottest fintech news of the previous week. This will include everything from funding rounds to trends to an analysis of a particular space to hot takes on a particular company or phenomenon. There's a lot of fintech news out there and it's my job to stay on top of it -- and make sense of it -- so you can stay in the know.

Financial services can benefit greatly from this technology, so let's talk about the different ways that IoT affects banking and financial decisions. IoT does serve a great purpose in the financial industry. In 2018, IoT spending in the banking and finance industry averaged roughly $153 million. IoT has already positively impacted the financial sector and will only continue to in the future. IoT saves financial companies a lot of time and money by gathering and transferring data.

Artificial Intelligence (AI) has taken a major role in acting as the main driver of upcoming hi-tech future in the world. It is shifting the information age to a completely new digital domain where upgraded machines help to solve critical decisions and assist in diverse sectors of a country. The banking and financial sector is very keen in investing in AI to protect their customers against the competitors in this market. Thus depending on the current scenario, Goldman Sachs, a leading American multinational investment bank, is betting on AI to dive growth in the economy. It has a fund of $72.5 million exclusively for the investment in Artificial Intelligence algorithms and data analytics.

Upside AI is one of the first funds in India to use machine learning to make fundamental investing decisions. The company was founded on the belief that technology will make better decisions than humans over the long term since machines are unbiased and unemotional decision-makers. Founded in 2018 by Kanika Agarrwal, Nikhil Hooda and Atanuu Agarrwal, Mumbai-based Upside AI uses technology to understand, recognise, and buy companies that are not only fundamentally good businesses but are also in-demand stocks. After two years in development, in July 2019 Upside AI came out of beta to start offering its investment products under a SEBI registered PMS license. For this week's startup column, we got in touch with one of the founders and Chief Investment Officer of Upside AI, Kanika Agarrwal to gain a more in-depth insight into how it drives AI and machine learning to provide fundamental investing. The algorithms then, over millions of portfolio iterations, learn how to pick companies that are not only fundamentally good businesses but also in-demand stocks.

Robots are impacting multiple industries, and the banking industry is no exception. Financial institutions across the world are beginning to use them for tasks ranging from counting money to handling customer requests in an effort to not only simplify tasks but to transform the branch experience. VTB, a Russian state bank, for example, has recently deployed robots designed by DIIP200 to handle cash-counting duties. An operator directs the robot to load cash from customer revenue, ATM cassettes and point-of-sale cash into trays. "From the moment of loading into special trays for recalculation to the moment of forming ready-made banknote spines, it works independently, performing all digital operations, including laying banknotes in the counting and sorting machine for recalculation, removing processed banknotes (spines of 100 sheets), placing them in the bander machine, and forming rolls with ruined bills and those subject to further examination," a spokesperson for VTB said in an email.

We've all come to terms with the fact that artificial intelligence (AI) is transforming how businesses operate and how much it can help a business in the long term. Over the past few years, this understanding has driven a spike in companies experimenting and evaluating AI technologies and who are now using it specifically in production deployments. Of course, when organisations adopt new technologies such as AI and machine learning (ML), they gradually start to consider how new areas could be affected by technology. This can range across multiple sectors, including production and logistics, manufacturing, IT and customer service. Once the use of AI and ML techniques becomes ingrained in how businesses function and in the different ways in which they can be used, organisations will be able to gain new knowledge which will help them to adapt to evolving needs.

These investments are paying off, according to fraud prevention specialists, as 80 per cent of experts say AI reduces payments fraud and 63.6 per cent of financial institutions cite AI as a valuable tool for halting fraud before it succeeds. These systems are commonplace at large banks that have more than $100 billion in assets -- 72.7 per cent of which leverage AI -- but only 5.5 per cent of all financial institutions reportedly have an AI-based system in place.

AI, big data, and machine learning have witnessed exponential growth over the past few years. With the evolving technology, businesses realize the importance of adopting AI and big data in their operations. AI, big data, and machine learning create exciting new opportunities for companies and entrepreneurs. But this rapid adoption is also partnered with several complexities and risks, hence, comes the need for regulations. Regulators and policymakers find it difficult to keep track of the constant developments in technology and AI systems.