The application of emerging technologies such as AI, cloud, blockchain and IoT in financial services has altered the traditional operating models of financial institutions, the competitive dynamics of the industry, the role of people in those institutions and the landscape of the financial system as a whole. In fact, AI is positioned as an essential investment, with the World Economic Forum arguing how it is set to become central to the fabric of financial institutions. While the adoption of AI in financial services may be in its infancy, the use cases are ever growing. From recommending loan and credit offerings to detecting fraud, 94% of financial services in European and Middle Eastern markets believe that AI will disrupt their business. The direction and the awareness of AI is clear but it is essential that companies invest now, as if done too hastily, the process is marred by pitfalls.
Artificial Intelligence has revolutionized the finance industry. Not only does it improve the precision level in the industry, but it also enhances the customer engagement level and speed up the query resolution period. In this blog, we will be finding out answers about the importance of AI in financial sectors or FinTech firms. By the year 2030, traditional financial institutions can shave 22% in costs, as per the latest 84-page report of the Autonomous in an AI in the financial industry. Fintech companies and financial firms were the early adopters of relational databases, mainframe computers, and have eagerly awaited the next generation of computational and analysis power.
Michelle Palomera, Global Head of Banking and Capital Markets at Rightpoint has experience with this. With over 25 years of experience in customer and digital consulting, Michelle combines practical industry and technology knowledge with a personalised style in working directly with clients and team members. Her extensive knowledge of financial services, which spans consumer, buy-side/wealth, commercial and institutional banking helps clients develop strategies for new revenue channels as well as launch new businesses through digital products and services. Here she explains how to de-bias AI in banking. When bias becomes embedded in AI software, financial institutions may unfairly reward certain groups over others, make bad decisions, issue false positives and diminish their opportunity. This will ultimately result in poor customer experience, decreased revenues and increased costs and risks.
During peak business periods for group carriers, such as open enrollment in the United States, artificial intelligence can be leveraged to increase group insurance sales by streamlining quoting, optimizing resources, automating manual tasks and eliminating duplication of effort before and during enrollment. Peak enrollment period is here once again as group and voluntary benefits providers put their remote work arrangements to the test in what will be an unusually demanding season. This year has been the year of digital transformation in the insurance industry, and 2020's challenges will inspire new approaches and digitization within carrier ecosystems. Fortunately, insurers can use AI and predictive analytics to increase group insurance sales. AI can help carriers streamline quoting and enrollment, optimize resources, and automate manual tasks.
CTech – Israeli business intelligence startup Intelligo Group, which has developed an automated due diligence and personnel background platform based on AI (artificial intelligence), has announced a $15 million Series B financing round led by Behrens Investment Group and including several existing investors. Intelligo has raised a total of $22 million to date. The company was founded in 2014 by CEO Shlomo Mirvis, Chief Research Officer Dana Rakovsky and COO Nadav Ellinson. Intelligo currently has more than 100 clients, including top corporations. The company employs 42 people and is in the process of adding to its workforce.
In 2009, the future founders of Kinetica came up empty when trying to find an existing database that could give the United States Army Intelligence and Security Command (INSCOM) at Fort Belvoir (Virginia) the ability to track millions of different signals in real time to evaluate national security threats. So they built a new database from the ground up, centered on massive parallelization combining the power of the GPU and CPU to explore and visualize data in space and time. By 2014 they were attracting other customers, and in 2016 they incorporated as Kinetica. The current version of this database is the heart of Kinetica 7, now expanded in scope to be the Kinetica Active Analytics Platform. The platform combines historical and streaming data analytics, location intelligence, and machine learning in a high-performance, cloud-ready package.
Just as businesses tap the value of machine learning, so too can charitable and non-profit organizations. There are a wide variety of ways people are applying machine learning for social good. Predict Align Prevent applies machine learning to identify children at risk for maltreatment. In the U.S., between 1,500 and 3,000 infants and children die due to abuse and neglect each year. Children aged 0-3 years are at the greatest risk. Those most vulnerable are commonly not visible to the professionals.
For investors looking for momentum, First Trust Nasdaq Artificial Intelligence and Robotics ETF ROBT is probably a suitable pick. The fund just hit a 52-week high and is up 96.8% from its 52-week low price of $22.51/share. Let's take a look at the fund and its near-term outlook to gain an insight into where it might be headed: This ETF seeks investment results that correspond generally to the price and yield, before the fees and expenses of the Nasdaq CTA Artificial Intelligence and Robotics Index. It has AUM of $137.5 million and charges 65 basis points in annual fees. Due to the coronavirus outbreak, the robotics market is flooded with opportunities as robots are being used for jobs such as sanitizing hospitals, homes and workplaces along with monitoring, surveying, handling, and delivering food and medicines.
Artificial intelligence has been a hot technology area in recent years and machine learning, a subset of AI, is one of the most important segments of the whole AI arena. Machine learning is the development of intelligent algorithms and statistical models that improve software through experience without the need to explicitly code those improvements. A predictive analysis application, for example, can become more accurate over time through the use of machine learning. But machine learning has its challenges. Developing machine-learning models and systems requires a confluence of data science, data engineering and development skills.
In Asia Pacific (APAC), adoption of artificial intelligence (AI) and machine learning (ML) in financial markets is accelerating. Though organizations in the Americas still lead in terms of AI/ML maturity and investment levels, those in APAC follow closely behind, according to a new research by Refinitiv, a leading provider of financial market data and infrastructure. Refinitiv, which surveyed more than 420 data scientists, quants, technology and data decision-makers, found that 69% of respondents in APAC view AI/ML as a core component of their business strategy, and 78% are making significant investment in AI/ML. COVID-19 is expected to further push adoption of AI/ML. According to the study, 31% of respondents in Asia said that AI/ML has become more important in their organization as a result of the pandemic, and 35% anticipate increased investment in AI/ML amid the public health crisis.