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Deep Learning and the Future of Finance

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Deep Learning and the Future of Finance

Deep learning is rapidly transforming the global financial services industry.

A step ahead of machine learning, deep learning focuses on finding minute details to function.

What is Deep Learning?

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Deep learning
 is a type of machine learning in AI that gathers huge datasets to make machines act like humans. Due to the use of neural networks, deep learning produces optimized results. You must have observed how Facebook automatically finds your friend in an image and suggests you tag her. Here, Facebook uses deep learning to recognize your friend. We were amazed to read what Gartner had to predict for about deep learning. It said, “Deep learning will soon provide best-in-class performance for demand, fraud and failure predictions.” Such a prediction encourages business leaders to implement deep learning for business and drive their business to greater success. While most business leaders are aware of the term, deep learning, they have very little to no understanding of the technology. Before leveraging deep learning for business, leaders should take a look at what deep learning offers and what the future of deep learning will look like.

Deep Learning in Finance

The financial world is chaning amid the covid-19 pandemic and the on-going war in Ukraine. Deep learning is increasingly deployed by financial services providers across industries within the financial sector. It has the potential to transform business models and markets for trading, credit and blockchain-based finance, generate efficiencies, reduce friction and enhance the product offerings. Deep learning in finance is about the sector overcoming its current challenges.

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The financial system is vital to any country as it directly focuses on the efficient functioning of the economy. Finance industry also provides a significant amount of employment to people across the globe. Apart from employing the masses, the finance industry also focuses on other parts of human life such as loan provision for businesses to invest in ambitious ventures. Apart from providing enterprises with financial help, the finance industry also focuses on risk management services that arrive with pooling of funds. Deep learning technology focuses on capturing abstract concepts to assist machines in concluding better decisions. According to Dr. Yoshua Bengio said in an interview to the Tech Emergence, deep learning is a technology that can contribute to helping people directly not just by building improved gadgets, but by directly influencing the human situation. Deep learning in finance is an application that the pioneers in the finance industry are vying for to solve the problems faced by them currently.

The State of The Finance Industry 

As we enter this next phase of the recovery, the future is unfolding. Financial services organizations are operating in a changing world—one they can help define. From banking and capital markets to insurance to investment management to commercial real estate, financial services firms face in 2022 a pivotal, no-turning-back opportunity to shape their—and the industry’s—future.

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Even after focusing on transforming lives and corporations, challenges faced by the financial institutions are numerous. One of the multiple challenges faced by finance industry is to keep up with the consumer laws. Keeping up with such laws and maintaining efficient functioning is always a challenge for finance companies across the world.

Another challenge commonly faced by financial organizations is the threat of a cyber-attack. Financial institutions are a prime target for attackers as it has the potential to make money for the hackers. Apart from making monetary benefits, attackers are also focusing on targeting the information held by such organizations. When an attacker gets access to a financial organization’s information, the threat of data theft always exists. One of the reasons CTOs and CIOs of financial organizations are on their toes to counter such attacks is because of the increase in the frequency of cyber-attacks on financial institutions in the last few years.

The Future of Deep Learning in Finance

Deep learning is a technology that learns from algorithms and focuses on making decisions based on an intensive investigation of data. As of today, the amount of information available to businesses and people is in abundance. With so much data being available, deep learning that uses neural networks can assist financial organizations in countering the challenges faced by them in numerous ways.

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With deep learning algorithms, organizations can feed the machines with patterns that are similar to cyber-attacks that are common. By learning from algorithms and having the availability of tremendous amounts of training data, deep learning can assist machines in determining a scenario of a cyber-attack in its initial phase itself.

Apart from merely using deep learning for countering cyber-attacks, the finance industry can leverage such technology even when they want to review commercial loans and form contracts accordingly. JPMorgan Chase is leveraging deep learning for the same purpose.

Deep learning has numerous applications across several industries. CTOs and CIOs of financial organizations should now find better ways of hiring deep learning experts to work on pre-existing data to achieve accurate information. Apart from hiring deep learning experts, businesses can also try to educate their employees on how to work with such a dynamic technology. Organizations could also focus on how they can revamp their infrastructure to welcome the wave of deep learning in their industry.


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TECHNOLOGY

Next-gen chips, Amazon Q, and speedy S3

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AWS re:Invent, which has been taking place from November 27 and runs to December 1, has had its usual plethora of announcements: a total of 21 at time of print.

Perhaps not surprisingly, given the huge potential impact of generative AI – ChatGPT officially turns one year old today – a lot of focus has been on the AI side for AWS’ announcements, including a major partnership inked with NVIDIA across infrastructure, software, and services.

Yet there has been plenty more announced at the Las Vegas jamboree besides. Here, CloudTech rounds up the best of the rest:

Next-generation chips

This was the other major AI-focused announcement at re:Invent: the launch of two new chips, AWS Graviton4 and AWS Trainium2, for training and running AI and machine learning (ML) models, among other customer workloads. Graviton4 shapes up against its predecessor with 30% better compute performance, 50% more cores and 75% more memory bandwidth, while Trainium2 delivers up to four times faster training than before and will be able to be deployed in EC2 UltraClusters of up to 100,000 chips.

The EC2 UltraClusters are designed to ‘deliver the highest performance, most energy efficient AI model training infrastructure in the cloud’, as AWS puts it. With it, customers will be able to train large language models in ‘a fraction of the time’, as well as double energy efficiency.

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As ever, AWS offers customers who are already utilising these tools. Databricks, Epic and SAP are among the companies cited as using the new AWS-designed chips.

Zero-ETL integrations

AWS announced new Amazon Aurora PostgreSQL, Amazon DynamoDB, and Amazon Relational Database Services (Amazon RDS) for MySQL integrations with Amazon Redshift, AWS’ cloud data warehouse. The zero-ETL integrations – eliminating the need to build ETL (extract, transform, load) data pipelines – make it easier to connect and analyse transactional data across various relational and non-relational databases in Amazon Redshift.

A simple example of how zero-ETL functions can be seen is in a hypothetical company which stores transactional data – time of transaction, items bought, where the transaction occurred – in a relational database, but use another analytics tool to analyse data in a non-relational database. To connect it all up, companies would previously have to construct ETL data pipelines which are a time and money sink.

The latest integrations “build on AWS’s zero-ETL foundation… so customers can quickly and easily connect all of their data, no matter where it lives,” the company said.

Amazon S3 Express One Zone

AWS announced the general availability of Amazon S3 Express One Zone, a new storage class purpose-built for customers’ most frequently-accessed data. Data access speed is up to 10 times faster and request costs up to 50% lower than standard S3. Companies can also opt to collocate their Amazon S3 Express One Zone data in the same availability zone as their compute resources.  

Companies and partners who are using Amazon S3 Express One Zone include ChaosSearch, Cloudera, and Pinterest.

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Amazon Q

A new product, and an interesting pivot, again with generative AI at its core. Amazon Q was announced as a ‘new type of generative AI-powered assistant’ which can be tailored to a customer’s business. “Customers can get fast, relevant answers to pressing questions, generate content, and take actions – all informed by a customer’s information repositories, code, and enterprise systems,” AWS added. The service also can assist companies building on AWS, as well as companies using AWS applications for business intelligence, contact centres, and supply chain management.

Customers cited as early adopters include Accenture, BMW and Wunderkind.

Want to learn more about cybersecurity and the cloud from industry leaders? Check out Cyber Security & Cloud Expo taking place in Amsterdam, California, and London. Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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TECHNOLOGY

HCLTech and Cisco create collaborative hybrid workplaces

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Digital comms specialist Cisco and global tech firm HCLTech have teamed up to launch Meeting-Rooms-as-a-Service (MRaaS).

Available on a subscription model, this solution modernises legacy meeting rooms and enables users to join meetings from any meeting solution provider using Webex devices.

The MRaaS solution helps enterprises simplify the design, implementation and maintenance of integrated meeting rooms, enabling seamless collaboration for their globally distributed hybrid workforces.

Rakshit Ghura, senior VP and Global head of digital workplace services, HCLTech, said: “MRaaS combines our consulting and managed services expertise with Cisco’s proficiency in Webex devices to change the way employees conceptualise, organise and interact in a collaborative environment for a modern hybrid work model.

“The common vision of our partnership is to elevate the collaboration experience at work and drive productivity through modern meeting rooms.”

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Alexandra Zagury, VP of partner managed and as-a-Service Sales at Cisco, said: “Our partnership with HCLTech helps our clients transform their offices through cost-effective managed services that support the ongoing evolution of workspaces.

“As we reimagine the modern office, we are making it easier to support collaboration and productivity among workers, whether they are in the office or elsewhere.”

Cisco’s Webex collaboration devices harness the power of artificial intelligence to offer intuitive, seamless collaboration experiences, enabling meeting rooms with smart features such as meeting zones, intelligent people framing, optimised attendee audio and background noise removal, among others.

Want to learn more about cybersecurity and the cloud from industry leaders? Check out Cyber Security & Cloud Expo taking place in Amsterdam, California, and London. Explore other upcoming enterprise technology events and webinars powered by TechForge here.

Tags: Cisco, collaboration, HCLTech, Hybrid, meetings

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Canonical releases low-touch private cloud MicroCloud

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Canonical has announced the general availability of MicroCloud, a low-touch, open source cloud solution. MicroCloud is part of Canonical’s growing cloud infrastructure portfolio.

It is purpose-built for scalable clusters and edge deployments for all types of enterprises. It is designed with simplicity, security and automation in mind, minimising the time and effort to both deploy and maintain it. Conveniently, enterprise support for MicroCloud is offered as part of Canonical’s Ubuntu Pro subscription, with several support tiers available, and priced per node.

MicroClouds are optimised for repeatable and reliable remote deployments. A single command initiates the orchestration and clustering of various components with minimal involvement by the user, resulting in a fully functional cloud within minutes. This simplified deployment process significantly reduces the barrier to entry, putting a production-grade cloud at everyone’s fingertips.

Juan Manuel Ventura, head of architectures & technologies at Spindox, said: “Cloud computing is not only about technology, it’s the beating heart of any modern industrial transformation, driving agility and innovation. Our mission is to provide our customers with the most effective ways to innovate and bring value; having a complexity-free cloud infrastructure is one important piece of that puzzle. With MicroCloud, the focus shifts away from struggling with cloud operations to solving real business challenges” says

In addition to seamless deployment, MicroCloud prioritises security and ease of maintenance. All MicroCloud components are built with strict confinement for increased security, with over-the-air transactional updates that preserve data and roll back on errors automatically. Upgrades to newer versions are handled automatically and without downtime, with the mechanisms to hold or schedule them as needed.

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With this approach, MicroCloud caters to both on-premise clouds but also edge deployments at remote locations, allowing organisations to use the same infrastructure primitives and services wherever they are needed. It is suitable for business-in-branch office locations or industrial use inside a factory, as well as distributed locations where the focus is on replicability and unattended operations.

Cedric Gegout, VP of product at Canonical, said: “As data becomes more distributed, the infrastructure has to follow. Cloud computing is now distributed, spanning across data centres, far and near edge computing appliances. MicroCloud is our answer to that.

“By packaging known infrastructure primitives in a portable and unattended way, we are delivering a simpler, more prescriptive cloud experience that makes zero-ops a reality for many Industries.“

MicroCloud’s lightweight architecture makes it usable on both commodity and high-end hardware, with several ways to further reduce its footprint depending on your workload needs. In addition to the standard Ubuntu Server or Desktop, MicroClouds can be run on Ubuntu Core – a lightweight OS optimised for the edge. With Ubuntu Core, MicroClouds are a perfect solution for far-edge locations with limited computing capabilities. Users can choose to run their workloads using Kubernetes or via system containers. System containers based on LXD behave similarly to traditional VMs but consume fewer resources while providing bare-metal performance.

Coupled with Canonical’s Ubuntu Pro + Support subscription, MicroCloud users can benefit from an enterprise-grade open source cloud solution that is fully supported and with better economics. An Ubuntu Pro subscription offers security maintenance for the broadest collection of open-source software available from a single vendor today. It covers over 30k packages with a consistent security maintenance commitment, and additional features such as kernel livepatch, systems management at scale, certified compliance and hardening profiles enabling easy adoption for enterprises. With per-node pricing and no hidden fees, customers can rest assured that their environment is secure and supported without the expensive price tag typically associated with cloud solutions.

Want to learn more about cybersecurity and the cloud from industry leaders? Check out Cyber Security & Cloud Expo taking place in Amsterdam, California, and London. Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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Tags: automation, Canonical, MicroCloud, private cloud

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