Big data in the finance industry will help overcome major challenges, gaining valuable insights to improve customer satisfaction and overall banking experience.

In today's digital world, organizations generate and collect data to improve transaction processing. Vast chunks of data get assimilated daily and it is essential to maintain them securely while meeting security constraints. Most banks fail to properly exploit the digital assets. It is crucial that the collected data must be put into good use as these bits of data can sometimes contain significant hidden information, which can drive to new opportunities. According to IBM, over 80% of data is dark data, expected to rise to 93% by 2020. However, every data that is generated and collected can be processed accurately with the use of big data analytics, which aim at analyzing, storing, querying, and updating substantial voluminous amounts of information. Big data in the finance industry has the potential to transform the finance industry by quickly assisting customers.

The finance industry faces specific problems with the traditional data management models. One of the most significant challenges faced are the fraudulent activities that are increasingly growing. The conventional model lacked to maintain the security of digital assets, which enabled hackers to achieve vital customer or industry data. Hence, there is a need to overcome this challenge at the earliest, since there is a high risk that the finance industry could burn out. Another major problem faced is the analysis of customers' sentiments. Most clients represent real wealth for any organization, and every industry must keep fulfilling their ever-increasing demands. The traditional model, however, does not provide any technique to analyze their customers. Moreover, conventional model lacked in segmenting their customers based on their transactions and other internal and external processes. Furthermore, business users cannot target the right audience for their marketing purpose with the traditional model. The list is endless. There is an urgent need for an advanced technology that could help banks overcome these challenges that are impacting their financial results.

As we are aware of the challenges that the finance industry faces with the traditional data management models, it is high time to leverage big data analytics to overcome the biggest issues. A few years back, a survey said that 84% of enterprises see big data analytics changing their industries' competitive landscapes. This states that big data analytics can transform various industries, including the finance industry. Big data analytics help the finance industry to maintain security and privacy of the collected data by the use of predictive analytics.

Predictive analytics help in predicting any fraudulent activity in the future, thereby helping detect hackers. Additionally, predictive analytics could be used to predict a machine breakdown, if any. Furthermore, big data analytics could be used by the finance industry for marketing purpose. Through social media platforms with sentiment analysis researchers can gather sentiments, opinions, and feedbacks of customers that can help them alter a business if itscustomers are unhappy. This will help banks to analyze their customer better and alter their business if necessary.

Furthermore, big data analytics help in customer segmentation, which allows the industry to find the right target audience. Banks can, thereby, boost their marketing skills and make maximum profits if they are able to properly leverage big data analytics. According to a recent IBM survey, more than 25% financial institutions are using big data analytics to stay ahead of the curve. Big data in finance has the potential to quickly identify real-time customer sentiments, prevent fraud, improve marketing tactics, and accelerate business growth.

SOURCE: https://www.bbntimes.com/en/technology/big-data-is-transforming-the-finance-industry

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SOURCE: http://www.thedrum.com/opinion/2018/05/21/how-airlines-can-leverage-flight-audiences-with-big-data-and-personalisation

rtificial intelligence or AI as it is more commonly referred to will have a strong economic impact in the U.A.E., a new study has found.

According to a report by Accenture, AI will boost economic growth within the U.A.E. by 1.6 percentage points, adding $182 billion to the national economy by 2035. The report examined the impact of using AI on 15 industries in the U.A.E. and 13 in Saudi Arabia.

As per the findings, AI was found to have the maximum impact on the financial services, healthcare, transport and storage industries with increases of $37 billion, $22 billion and $19 billion, respectively, in their annual gross value added (GVA), which measures the output value of all goods and services in a sector.

AI is also set to lead to an increase of $6 billion and $8 billion in GVA within labor intensive industries such as education and construction respectively, the report found. Meanwhile in Saudi Arabia, AI will increase GVA by $215 billion.

The findings come as the Middle East and specifically the U.A.E. look to AI to diversify away from its dependence on oil revenues.

Last year, the U.A.E. revealed an artificial intelligence strategy that largely aims to speed up the government’s productivity and improve the performance of all official departments. The country also appointed a minister for artificial intelligence to spearhead the development of its AI strategy.

Within the U.A.E., the emirate of Dubai has been at the forefront of automating processes. Dubai has introduced robo-cops, aiming to transform nearly 25% of its patrolling taskforce to robots by 2030. The emirate has also started testing the world’s first autonomous taxi-pods.

Meanwhile Saudi Arabia recently awarded a robot its citizenship, signaling its willingness to embrace AI as it develops its non-oil economy.

“The level of growth that AI stands to bring to the U.A.E.’s economy is unparalleled,” said Amr El Saadani, managing director of Accenture’s Financial Services practice in the Middle East and Turkey. “The nation’s leaders already understand the impact of this powerful technology, evidenced by the appointment of the first Minister of AI last year.

“While AI-led growth will be felt across a wide variety of industries, the financial services sector has the most to gain — which isn’t surprising, given that many of its jobs can be significantly augmented with AI and machine learning. In addition, Accenture reports have already shown that banking executives globally are taking action to transform their businesses through the use of AI.”

SOURCE: https://www.forbesmiddleeast.com/en/naguib-sawiris-beltone-financial-to-acquire-significant-stake-in-oragroup/

“Artificial intelligence (AI) is coming to get us” and it’s just a small step from a self-driving car to AIs taking over our world and the “end of days” dystopias that Hollywood has popularised in a number of recent films. Or so some would have us believe.

The reality is far less dramatic, and AI will need to go through a number of stages of development before it reaches the most extreme scenarios – if ever. In our recent book Beyond Genuine Stupidity – Ensuring AI Serves Humanity, we explore the possibilities and challenges presented by this game changing technology. In this article we draw on key concepts from the book to demystify the fundamentals of AI.

What is Artificial Intelligence and why now?

At the core of the concept of AI is the idea of developing intelligent machines - e.g. computer algorithms that work and react like humans. Applications include performing speech recognition, natural language processing and translation, visual perception, learning, reasoning, inference, strategising, planning, intuition, and decision-making. People have been working on these concepts since the 1940’s and AI has experienced many false dawns or “winters” as the AI community likes to describe them.

This time round, the feeling is that AI is here to stay. There are five main factors that have made AI the hot topic on the agenda for companies, investors, politicians, and citizens alike. 

Firstly, we are seeing the development of far more efficient and smarter machine learning tools – the core algorithms through which AI systems develop their intelligence. 2) In parallel, computer hardware processing power and transaction speeds have accelerated, and 3) cloud computing allows us to share and combine data and processing power across the globe. 4) At the same time companies such as Google and Amazon have amassed vast amounts of data that require AI to process it – giving rise to 5) money. The scale of the opportunity presented by AI has seen billions of dollars being invested by these new technology companies, corporates in other sectors, and venture funded start-ups – so the game is well and truly on.

Core Fields of Artificial Intelligence

There are a number of different fields which fall under the broad umbrella of AI, the main ones are described below. The most common are rule based or “expert” systems which can apply vast number of rules on a consistent basis. These have been around for over 30 years with common applications including the processing of loans and mortgage applications and basic medical diagnosis.

Robotic Process Automation (RPA) is the next stage on, with machines performing complete processes that in the past required a combination of rule based decision making and an element of human judgement – common in tasks such as handling calls to service centre, processing an insurance claim, or checking the calculations in an architectural design.

Tasks considered closer to true human intelligence include voice recognition, interpretation of human conversations conducted in “natural” languages, and speech generation. Similarly, considerable progress has been made with computer vision – enabling the ever-more accurate recognition of images – with application as diverse as spotting misshapen pies on a production line through to facial recognition for airport security purposes. Other increasingly deployed AI applications include tools that can undertake sophisticated planning, scheduling, and optimisation of tasks - encompassing everything from manufacturing to crew rostering.

The hottest field of AI at present is Machine Learning - the notion of using statistical techniques to help systems learn from data. This is the area gaining most attention at present because of its potential – namely the use of neural networks and deep learning algorithms to develop systems with learning capabilities closer to those of the human brain. These tools are being used across a range of applications, with the most common being chatbots to perform increasingly sophisticated customer interaction tasks by combining machine learning, natural language processing, and speech generation.

Finally, robotics is being put to widespread use in everything from warehouse management and surgery to cooking burgers in restaurants. Robots typically combine a number of the other Ai fields to provide smart mechanoids.

As we said time round, the feeling is that AI is here to stay. It could become central to every aspect of human existence from detecting and treating heart failure through to running companies, economies, and legal systems.

Rohit Talwar, Steve Wells, Alexandra Whittington, April Koury, and Helen Calle, of Fast Future

SOURCE: https://www.gigabitmagazine.com/ai/artificial-intelligence-reality-beyond-movies