banking & finance


TD-Ameritrade-launches-Facebook-chatbot.html?ITO=1490&ns_mchannel=rss&ns_campaign=1490

Daily Mail

TD Ameritrade's chatbot uses artificial intelligence and machine learning to respond to natural language messages from users on Facebook Messenger about their investments and stock prices, the brokerage said on Tuesday. Unlike other chatbots, TD Ameritrade's bot will be backed by a human customer support team, which will receive notifications if the bot detects it is unable to handle some interactions. Unlike other chatbots, TD Ameritrade's bot will be backed by a human customer support team, which will receive notifications if the bot detects it is unable to handle some interactions. TD Ameritrade's chatbot uses artificial intelligence and machine learning to respond to natural language messages from users on Facebook Messenger about their investments and stock prices.


Dimensional Reduction and Principal Component Analysis -- II

@machinelearnbot

The features of the data set are Date, Open pricing, the maximum price reached during the day, minimum price during the day, last price before closing, closing price, total traded quantity and turn over in lakhs. To get the feeling of how the different stocks are distributed along opening stock prices and total trade quantities, let us visualize them using histograms. Let's call them λ and W. Therefore, in terms of principal component analysis, we will say that the scores are the product of matrices X and W, i.e., Finding the Eigen values and Eigen vectors are computationally not the most efficient way of finding the loadings or the matrix W. We have a shortcut to do the same thing and that is through Singular Value Decomposition or SVD. This is possible since we are only choosing the top 2 Eigen vectors with the highest Eigen values to construct our (150 x 2) Eigen vector matrix W. You can see that the values are in descending order.


The new spring of artificial intelligence

#artificialintelligence

Companies need to have started the digital journey to be ready to adopt AI; in fact, the cross-industry correlation between our digital index (a measure of digital assets and usage developed in Digital Europe by MGI) and the same measure adapted to build the AI index (a measure of AI investments and usage) is up to 0.55, and strongly significant. The analysis at the firm level confirms the industry lens of a link between digital maturity and AI adoption, and between profit expectations and pace of AI adoption. We used variables described in points 2 through 4 as valid instrumental variables (IV) correlated with AI adoption, to estimate the effect of AI adoption on profit margin development of companies (we omitted the variable "size" as well as the variable "expected benefits" for IV because both could be correlated directly to profit margin development; typical validity tests confirm our list of IV variables cannot be rejected). Using an output view of profit function, it is well known that growth in technical change, or total productivity growth, is a function of profit deployment as well as the of expansion of output in the long-term (in the short term, we should add a capacity utilisation term; see Karagiannis and Mergos 2000).


Artificial Intelligence Needs a Strong Data Foundation

#artificialintelligence

Similar to Maslow's hierarchy, data science advisor Monica Rogati has developed a similar pyramid to illustrate that while most firms are striving for the top of the data science hierarchy of needs (artificial intelligence), many more basic requirements must first be met. Remember, in many cases, the application of your AI and deep learning will be to improve the customer's banking experience, provide proactive financial recommendations and/or be applied to fraud and risk avoidance. Transforming data into insights is the highest stage that many financial services organizations ever reach in the data pyramid. But if you are collecting the needed real-time data, that is organized, clean, tested and optimized, it is time to test machine learning and artificial intelligence solutions.


Next-Gen Technology Separates Digital Leaders From The Rest

#artificialintelligence

According to the SAP Center for Business Insight report "SAP Digital Transformation Executive Study: 4 Ways Leaders Set Themselves Apart," in collaboration with Oxford Economics, 97% of the 3,100 surveyed executives are not successful in realizing that digital vision. Big Data and analytics, the Internet of Things, and machine learning: a higher percentage of digital leaders are integrating these innovations into their core infrastructure. "For example, the SAP Digital Transformation Executive Study found that nearly 50% of top executives in the manufacturing industry see investment in digital skills and technology as the most important revenue growth driver in the next two years. For more insight on digital leaders, check out the SAP Center for Business Insight report, conducted in collaboration with Oxford Economics, "SAP Digital Transformation Executive Study: 4 Ways Leaders Set Themselves Apart."


FinTech, AI and the fourth Industrial Age

#artificialintelligence

To remain competitive, there is a growing need to use and master complex AI tools, adapt to new forms of convergence through collaboration and develop meaningful client relationships through new forms of customer centricity. Though banking has a long history of resisting modern methodologies -- agile development, cloud computing, advanced analytics, predictive onboarding, open platforms, hypertargeting and external data harvesting -- AI is one area the industry simply must embrace. If the FinTech industry fails to be more open to building new forms of customer value, efforts toward leveraging broader platforms will simply fail to materialize. Advanced tools now provide the industry with more capabilities to provide intelligent, personalized advice to offer new forms of customer advocacy beyond traditional services.


Time prime in worker-scarce Japan for investing in service robots

The Japan Times

Faced with the worst labor shortage in decades, Japanese service companies are finally turning to labor-saving technology, an investment that could lift the sector's woeful level of productivity and allow them to raise wages. It plans to spend about ¥300 million ($2.7 million) to install new technology at its 15 nursing homes in and around Tokyo to make life easier for staff and residents. Capital spending is the most important factor in Japan's productivity growth, Goldman Sachs economists wrote in a recent report. Izumi Devalier, head of Japan economics at Bank of America Merrill Lynch in Tokyo, said the labor shortage could turn out to be an opportunity, forcing Japanese service-sector companies to finally start investing, and perhaps fueling an economic revival.


Is artificial intelligence the future of finance?

#artificialintelligence

AI has also been the subject of a recent European Commission (EC) consultation document, to which CFA Institute submitted a response. This'training' involves using a large training data set that the computer algorithm can repeatedly go through (but typically with guidance and supervision) to learn through trial and error how to connect the input data (e.g., credit history, employment history, assets, purchasing history) with the desired output (e.g., the correct identification of a suitable risky portfolio). Although some attempts have been made to check the source code of algorithmic traders, the most effective protection against algorithmic errors are circuit breakers on markets that limit the amount of damage a failing algorithm can cause. Consider attending the CFA Institute European Investment Conference, held in Berlin this November.


Artificial Intelligence and Fintech

#artificialintelligence

The epic rise in Fintech industry is attributed to the rise of industry in China which contributed a lot in bringing this boom. The answer is, cutting edge technology which brought together the finance and technology together, along with a little addition of artificially intelligent machine learning algorithms which made the recommender systems more effective and efficient. These auto-bots use several complex machine learning algorithms to understand the financial curve of the stocks which are being sold and bought. For organizations, Fintech and AI have introduced merchant solutions and several fraud detection modules which are of great use to the industry.


Banking Chatbots – Chatbots Magazine

#artificialintelligence

According to a report, it is expected that there will be around 1.2 billion mobile banking users worldwide by the end of 2016. According to a report released by Gartner, consumers will manage 85% of the total business associations with banks through Fintech chatbots by 2020. The artificial assistant helps a customer to save their money. If you enjoyed the story, you can read the whole story on Banking chatbots and its benefits for the industry here:"How Chatbots are transforming Wall Street and Main Street Banks?"