ASAPP founder Gustavo Sapoznik developed software that trains customer-service reps to be "radically" more productive, winning the young startup an $800 million valuation. If you've ever felt your blood boil after sitting on hold for 40 minutes before reaching an agent . . . A customer-service representative for JetBlue, for instance, might have to flip rapidly among a dozen or more computer programs just to link your frequent-flier number to a specific itinerary. "Imagine that cognitive load, while you have someone screaming at you or complaining about some serious problem, and you're swiveling between 20 screens to see which one you need to be able to help this person," says Gustavo Sapoznik, 34, the founder and CEO of ASAPP, a New York City–based developer of AI-powered customer-service software. Sapoznik remembers just such a scene while shadowing a call-center agent at a "very large" company (he won't name names), watching the worker navigate a "Frankenstack" patchwork of software, entering a caller's information into six different billing systems before locating it.
As our intrinsic nature, we humans are likely to form an opinion about a particular commodity or person even before we have shared any real-life experience with them. The same thing happens for a business brand; we tend to have developed some sort of subconscious thoughts regarding the brand prior to using its products and services. This bias severely impacts how businesses perform in the market and their sales figures. So it is safe to assume that how brands present themselves or appeal customers play a decisive role in determining their success. Now one may wonder how companies can figure out what the customers feel or react to their products; well, this is where emotional analytics comes in. Though data-driven Analytics provides a quick shorthand to businesses, without emotional insights, brands are a handicap.
The banks hopes to streamline processes and protect clients from fraud. Indonesia's Bank Central Asia (BCA) will utilise data cloud firm Cloudera to boost operational efficiency and customer engagement, according to an announcement. The bank hopes that Cloudera will help them aggregate structured and unstructured data from emails, social media and call centres, as well as shorten the time taken for queries. Cloudera's data platform has also enabled BCA to implement machine learning processes for automation. As a result, the bank's business units have gained a holistic view of their customers and are using near real-time insights to provide personalised offerings based on customer profiles.
A math expert who studied number theory at the University of Tokyo's graduate school, he made an impression earlier in his career by heading the bank's launch of bond options trading. Digitalization is key to streamlining operations, especially in domestic retail banking, he said. While MUFG and others saw a surge in branch traffic despite a stay-at-home plea by the government in April, Kamezawa said the bank is now seeing a jump in the use of online banking services. His two predecessors, Kanetsugu Mike and Nobuyuki Hirano, were known for their overseas backgrounds. Under them, MUFG spent about $15 billion to acquire PT Bank Danamon Indonesia and Thailand's Bank of Ayudhya, and to obtain stakes in Vietnam's Vietinbank and Security Bank Corp. of the Philippines. Asked whether the acquisition of commercial banks in the region was over, Kamezawa said: "I think so." "We have succeeded in making up for declining domestic profit through our push overseas," he said, adding that the priority now was to control steadily rising costs. MUFG's costs as a percentage of revenue remain high, standing at 70.2 percent for the year ended March, compared with 62.8 percent for rival Sumitomo Mitsui Financial Group Inc. "We will recalibrate our global strategy, review growth areas and allocate resources accordingly," Kamezawa said. Since the early stages of the COVID-19 crisis, The Japan Times has been providing free access to crucial news on the impact of the novel coronavirus as well as practical information about how to cope with the pandemic. Please consider subscribing today so we can continue offering you up-to-date, in-depth news about Japan.
In this paper, we study the volatility forecasts in the Bitcoin market, which has become popular in the global market in recent years. Since the volatility forecasts help trading decisions of traders who want a profit, the volatility forecasting is an important task in the market. For the improvement of the forecasting accuracy of Bitcoin’s volatility, we develop the hybrid forecasting models combining the GARCH family models with the machine learning (ML) approach. Specifically, we adopt Artificial Neural Network (ANN) and Higher Order Neural Network (HONN) for the ML approach and construct the hybrid models using the outputs of the GARCH models and several relevant variables as input variables. We carry out many experiments based on the proposed models and compare the forecasting accuracy of the models. In addition, we provide the Model Confidence Set (MCS) test to find statistically the best model. The results show that the hybrid models based on HONN provide more accurate forecasts than the other models.
As the B2B (Business-to-Business) sales landscape has evolved, it offers the opportunity to harness the value of Artificial Intelligence (AI) in contact centers. In-house sales teams using CRM in the call center now have access to a wide range of automation and AI solutions that reduce costs and generate efficiencies that can increase sales closing rates. In fact, Gartner predicts that by 2020, 30% of all B2B companies will use Artificial Intelligence to augment at least one of their main sales processes. To do this, you first have to know what AI solutions to invest in, and how those investments can work with the proper integration of a CRM (Customer Relationship Management). They are two fundamental aspects to use them successfully and achieve a high ROI (Return On Investment). It is a connection between the CRM system and the software of a call center that automatically transfers data so that each system, and the people and processes that use it, work better.
Dmitry Dolgorukov is the Co-Founder and CRO of HES Fintech, a leader in providing financial institutions with intelligent lending platforms. Artificial intelligence survived the early stages of the maturity cycle and reached the plateau of productivity to the extent that Andrew Ng claimed, "AI is the new electricity." Stanford University indicates that the number of active AI-based startups increased by 1,400% between 2000 and 2017. In this regard, Forbes cites research findings revealing that AI-associated startups attract up to 50% more in funds than "ordinary" technological companies. If you want an analogy, it's the Gold Rush, but digital.
Digital transformation will create trillions of dollars of value. While estimates vary, the World Economic Forum in 2016 estimated an increase in $100 trillion in global business and social value by 2030. Due to AI, PwC has estimated an increase of $15.7 trillion and McKinsey has estimated an increase of $13 trillion in annual global GDP by 2030. We are currently in the middle of an AI renaissance, driven by big data and breakthroughs in machine learning and deep learning. These breakthroughs offer opportunities and challenges to companies depending on the speed at which they adapt to these changes.
Therefore, banks must leverage AI to balance the need for privacy and security with personalisation and engagement. Creative implementation of AI by start-ups and fintechs has helped further this trend. From personalisation to customer service, fraud detection and prevention to compliance, and risk monitoring to intelligent contract documents, AI has helped banks gain better control and predictability.Related NewsToday, customers expect faster, personal, and meaningful services and interactions with their banks and little tolerance for generic unsolicited messages. Therefore, banks must leverage AI to balance the need for privacy and security with personalisation and engagement. That said, the Indian banking sector has some amount of catching up to do.While Indian banks have explored the use of AI, it has primarily been used to improve customer experience by adding chatbots as an additional interface for customers like SIA by State Bank of India, Eva by HDFC and iPal by ICICI.