• Developers can save a lot of time and money by using pre-trained foundation models instead of having to start from scratch when training a language model.
• The first is a generative LLM for information extraction, open-ended question and answer, classification, text generation, and summarization.

Amazon Web Services Inc. has recently expanded its reach into artificial intelligence software development by releasing several new tools for generative AI training and deployment on its cloud platform.

The business described new services in a post on the AWS Machine Learning blog, including the capacity to build and train foundation models, which are extensive, pre-trained language models that lay the groundwork for particular natural language processing tasks.

Deep learning techniques are generally used to train foundation models on enormous volumes of text data, enabling them to become adept at understanding the subtleties of human language and produce content nearly indistinguishable from that written by humans.

When training a language model, developers can save time and money using pre-trained foundation models instead of starting from scratch. A foundation model for text generation, sentiment analysis, and language translation is the Generative Pre-trained Transformer (GPT) from OpenAI LLC.

LLM Choices

Bedrock’s brand-new service makes foundation models from various sources accessible through an API. The Jurassic-2 multilingual large language models from AI21 Labs Ltd., which produce text in Spanish, French, German, Portuguese, Italian, and Dutch, and Anthropic’s PBC’s Claude LLM, a conversational and text processing system that follows moral AI system training principles are included. Users can use the API to access Stability AI Ltd. and Amazon LLMs.

According to Swami Sivasubramanian, Vice President of database, analytics, and machine learning at AWS, foundation models are pre-trained at the internet scale. They can therefore be customized with comparatively little additional training. He used the example of a fashion retailer’s content marketing manager, who could give Bedrock as few as 20 examples of effective taglines from past campaign examples with relevant product descriptions. Bedrock will then automatically generate effective social media posts, display ad images, and web copy for the new products.

In addition to the Bedrock announcement, AWS is releasing two new Titan large language models. The first is a generative LLM for information extraction, open-ended question and answer, classification, text generation, and summarization. The second LLM converts text prompts into numerical representations, including the meaning of the text and helps build contextual responses beyond paraphrasing.

No mention of OpenAI, in which Microsoft Corp. is a significant investor, was made in the announcement. Still, given the market’s demand for substantial language models, this shouldn’t be a problem for Amazon.

Although AWS is behind Microsoft and Google LLC in bringing its LLM to market, Kandaswamy argued that this shouldn’t be considered a competitive disadvantage. He said, “I don’t think anyone is so behind that they have to play catchup. It might appear that there is a big race, but the customers we speak with, other than very early adopters, have no idea what to do with it.”

Hardware Boost

Additionally, AWS is upgrading its hardware to provide training and inference on its cloud. New, network-optimized EC2 Trn1n instances now offer 1,600 gigabits per second of network bandwidth, or about a 20% performance increase, and feature the company’s exclusive Trainium and Inferentia2 processors. Additionally, the business’s Inf2 instances, which use Inferentia2 for inferencing of massively multi-parameter generative AI applications, are now generally available.

CodeWhisperer, an AI coding companion that uses a foundation model to produce code suggestions in real-time based on previous code and natural language comments in an integrated development environment, is another product whose availability has been announced. The tool is accessible from some IDEs and supports Python, Java, JavaScript, TypeScript, C#, and ten other languages.

Sivasubramanian wrote, “Developers can simply tell CodeWhisperer to do a task, such as ‘parse a CSV string of songs’ and ask it to return a structured list based on values such as artist, title and highest chart rank.” CodeWhisperer produces “an entire function that parses the string and returns the list as specified.” He said that developers who used the preview version reported improvement of 57% in speed with a 27% higher success rate.

As many players attempt to capitalize on the success of proofs of a concept like ChatGPT, the LLM landscape will likely remain dispersed and chaotic for the foreseeable future. As Google’s Natural Language API has in speech recognition, it’s unlikely that any one model will come to dominate the market, according to Kandaswamy.

He said, “Just because a model is good at one thing doesn’t mean it’s going to be good with everything. It’s possible over two or three years everybody will offer everybody else’s model. There will be more blending and cross-technology relationships.”

• Amazon Web Services, Inc. has announced call analytics for the Amazon Chime software development kit, making real-time audio call insights easier and cheaper to gather.
• Amazon Chime SDK is a collection of components for adding a message, audio, video, and screen-sharing features to online and mobile apps.

Amazon Web Services Inc. recently said it would enable call analytics for the Amazon Chime software development kit, making collecting insights from real-time audio calls more accessible and less expensive.

According to the firm, Amazon Chime SDK call analytics helps developers construct real-time call transcriptions, analyze speech tone, and search for specific speakers. AWS is also enhancing the Amazon Chime SDK section of the AWS Management Console to facilitate incorporating these new features into audio apps.

Amazon Chime SDK is a collection of components for adding message, audio, video, and screen-sharing features to online and mobile apps in real-time. For instance, developers may utilize it to integrate video into a health app, allowing consumers to initiate a video conversation with their doctor straight through the app.

Sébastien Stormacq, Amazon’s principal developer advocate, stated that there are instances in which customers may wish to record and analyze calls conducted through their audio apps. The new voice analytics capability is intended to produce real-time insights based on these calls by extracting emotion from speech cues to detect displeasure and impatience. It operates by analyzing lexical and linguistic information, i.e., what was said, in conjunction with acoustic and tone information, i.e., how it was said.

According to Stormacq, this speech tone analytics will be provided to the customer’s chosen data lake, allowing them to develop dashboards capable of visualizing this data.

Amazon illustrated how this might operate in the financial business, where trading room supervisors frequently record all talks for regulatory purposes. Stormacq stated that voice tone analysis would assist supervisors in monitoring for risk and compliance. In addition, the insights can help traders in increasing their efficiency.

Healthcare, the public sector, telecommunications, and insurance are some businesses that might benefit from voice analysis.

The new speaker search function is intended to discover and identify call participants. Based on a database of known voices, it can identify particular speakers after hearing only a few seconds of their speech. This, according to Stormacq, can assist with duties such as accelerating caller lookup and enhancing call transcripts with identity identification.

Amazon has upgraded the AWS Management Console to make it simpler for developers to utilize these new features. To evaluate a call, developers may simply select the AWS artificial intelligence service, such as voice analytics, Amazon Transcribe, or Amazon Transcribe Call Analytics.

To view these insights, customers may choose Amazon QuickSight or Tableau, whose dashboards can then be incorporated into various apps, wikis, and portals, according to Amazon. Developers may obtain pre-built dashboards as AWS CloudFormation templates rather than developing their own.

Finally, call analytics may be used to produce real-time alerts by publishing specified events to Amazon EventBridge, which then forwards these notifications to the user’s AWS account or any supported third-party application.

Constellation Research Inc.’s Liz Miller told a leading media house that developers would embrace the new call analytics features in Chime since speech and emotion data of this type is essential for training new AI and machine learning models.

Liz Miller said, “This latest SDK gives Chime and AWS customers exactly what they need to turn voice into action. While AI and ML have been announced by almost every major contact center provider, the interesting part of the AWS announcement is that this is really about providing teams building tomorrow’s experiences with the developer toolkits they need to accelerate time to value.”

Amazon stated that Amazon Chime SDK call analytics fees will be usage-based, with customers being charged per minute of audio data examined. Amazon Chime SDK call analytics are presently available in Asia Pacific (Singapore), the US East (Ohio, N. Virginia), and Europe (Frankfurt) regions of AWS, with additional areas to be added in the future.

• TigerGraph Inc. is upgrading TigerGraph Cloud, its cloud-based graph database, with new capabilities meant to make it easier for users to collect and analyze data.

TigerGraph Inc. is upgrading TigerGraph Cloud, its cloud-based graph database, with new capabilities to make it easier for users to collect and analyze data.

The business released further information on the change recently. It also revealed that the number of organizations utilizing its software has climbed dramatically over the last year. According to the company, the installed base of TigerGraph Cloud has more than quadrupled in the previous year.

Jay Yu, vice president of product and innovation at TigerGraph, said, “Graph is a crucial tool for solving business challenges, and TigerGraph is committed to helping customers unlock the full potential of their data by using ML and AI to close the gap between data and decisions.”

Many analytics projects need awareness of the relationships between the processed records. For instance, a store may assess if two e-commerce purchase records may be linked to the same consumer—a more profound comprehension of what clients purchase helps merchants provide more relevant offers.

Identifying correlations between data elements is also crucial for various other analytics applications. These use cases cover several industries, including cybersecurity, financial services, and logistics.

TigerGraph states that standard databases are frequently not designed to analyze record connections. Several can do the work, but not with speed required for demanding analytics applications.

TigerGraph Cloud is a graph database. This database is developed mainly to store and analyze relationships between records effectively. TigerGraph Cloud is one of the category’s most popular platforms.

Only data stored in a format supported by the database can be imported into it. Apache Parquet has been added to the list of supported data formats for the TigerGraph Cloud. Parquet, which was initially introduced in 2013, is frequently utilized by businesses since it compresses data and minimizes the storage space required for analytics projects.

TigerGraph Cloud’s newly added support for the format should result in a more intuitive usability. As Parquet is extensively utilized and many developers are already familiar, importing data into TigerGraph Cloud should become simpler. In addition, the firm has revamped the platform’s data-loading interface to make it easier to use.

The Parquet compatibility is launching with the expanded support for Kubernetes, another ubiquitous open-source technology.

The default feature set of Kubernetes may be expanded by constructing extensions that automate container management operations. Such tailored extensions are referred to as operators. As part of the recent release, TigerGraph Cloud now supports Kubernetes operators that facilitate growing container clusters and data backup chores.

It consists of a collection of monitoring instruments for detecting possible problems. The firm is currently updating these tools to make it easier for administrators to monitor the health of their organizations’ database deployments. Moreover, it facilitates tracking user searches and attempts to load new entries into the database.

TigerGraph Cloud’s backend has undergone significant security and reliability enhancements. According to the business, the upgrades were motivated by findings from the platform installations of numerous significant customers. In addition, several customized improvements have been implemented to facilitate specific data visualization and time-series data analysis activities.

TigerGraph Cloud is offered in a managed edition and a version that businesses may implement inside their infrastructure-as-a-service setups. The database is compatible with all three largest public cloud platforms. TigerGraph also provides a version of its software that may run on-premises for businesses that manage their data centers.

• The new partnership will allow developers to deploy neural networks on SageMaker in just a few clicks.
• Amazon SageMaker and AWS-designed chips will let the team and larger machine learning community turn the latest research to reproducible models that anyone can build on.

Hugging Face Inc., the operator of a well-known platform that hosts machine learning models, is collaborating with Amazon Web Services Inc. to streamline the development of its artificial intelligence projects.

There was an existing collaboration going on since early 2021 which got expanded when the companies announced the partnership on February 21, 2023.

Adam Selipsky, AWS Chief Executive Officer, said, “Generative AI has the potential to transform entire industries, but its cost and the required expertise puts the technology out of reach for all but a select few companies. Hugging Face and AWS are making it easier for customers to access popular machine learning models to create their own generative AI applications with the highest performance and lowest costs.”

New York-based Hugging Face has received over USD 160 million in a recent funding round. It operates a platform that resembles GitHub, which helps developers to host open-source AI models plus technical assets such as training datasets. It allows storing code for over 100,000 neural networks.

Additionally, Hugging Face will use AWS as its preferred public cloud in accordance with the new collaboration. The business is also launching a new integration with the machine learning platform Amazon SageMaker. Developers can use the platform’s more than six cloud services to train, deploy, and create AI models.

With just a few clicks, developers will be able to deploy neural networks hosted by Hugging Face on SageMaker thanks to the new integration. After uploading an AI model in SageMaker, it can be trained by using AWS Titanium chips-powered cloud instances. The chips are curated exclusively for AI training tasks.

Neural networks deployed from Hugging Face to AWS work with many types of cloud instances, including the ones powered by AWS Inferentia accelerator series. These are basically the chips optimized to perform inference, or the tasks of running AI models right after the training phase gets completed.

Clement Delangue, CEO of Hugging Face, said, “The future of AI is here, but it’s not evenly distributed. Amazon SageMaker and AWS-designed chips will enable our team and the larger machine learning community to convert the latest research into openly reproducible models that anyone can build on.”

It adds essence to Hugging Face AWS Deep Learning Containers that the company already offers to developers as a part of the partnership. The containers make Hugging Face’s AI models available in prepackaged format which is easy to deploy in public cloud environments.

• Technology advancements, consumer demands, and many other factors have influenced mobile app development trends.
• Below points are empirical research to come up with the top mobile app development trends that will dominate 2023.

2022 was a noteworthy year for technical advancement. The mobile app development business is one such industry that is in a constant state of change. Technology advancements, consumer demands, and many other factors have directly influenced mobile app development trends.

Nonetheless, the question persists. What are the latest trends in mobile app development? What application development trends will be prevalent in 2023? What is the future of developing mobile applications?

Have you ever wondered how many applications are on your mobile device? Many, right? Also, a new feature is introduced almost every day. Research has shown that an average user spends over 90% of their time on mobile applications.

According to Statista, the global income from mobile applications surpassed USD 318 billion in 2020. Compared to 2019, there was a rise of over USD 60 billion. The sector with the most income that year was mobile games, with over USD 200 billion, followed by social networking mobile applications, with over 31 billion dollars.

The Statista Digital Market Outlook predicts that revenue will expand across most market sectors over the next several years, reaching around USD 613 billion by 2025. Mobile applications are replacing websites, and in the near future, it will be a certainty that every business has an app.

There are more than 50 million applications accessible, and the number continues to expand each day. Everyone grew connected to the digital world during the pandemic, and applications became the standard.

So, are you prepared for the mobile app development changes in the coming year?

The list below is more than just a hunch. Below points are empirical research to come up with the top mobile app development trends that will dominate 2023.

Top 7 Mobile App Development Trends for 2023

5G Integration

The introduction of 5G will significantly influence app patterns in 2023. 5G has ushered in a new phase of the digital revolution. With the enhancement of the cellular network, the creation of apps that employ cutting-edge technology will increase significantly. This covers AR and VR, IoT, gaming, and streaming apps.

For developers, resellers, and producers, this technology is set to revolutionize the usage and creation of mobile applications.

In 2021, there were around 3.5 times as many 5G connections as in 2020. Between 2022 and 2023, connections are expected to be twofold.

Moreover, 5G enables mobile app developers to offer more features and functionalities, enhancing the user experience.

5G will enhance digital transactions, advanced security, lightning-fast response, and tailored healthcare, among other things. It will assist developers in creating more mobile applications based on these trends and technology.

Augmented Reality (AR) and Virtual Reality (VR)

Pokemon Go’s short popularity cleared the mobile app development path for augmented reality (AR). It demonstrated to the world that virtual reality (VR) could provide consumers with an immersive experience.

Developing a mobile app that includes AR and VR is anticipated to go to a higher degree of digital advancement. The market based on these technologies is expected to reach USD 160 billion by 2023, and applications will account for a larger share of this. The tourism, entertainment, and gaming industries will benefit the most.

Below mentioned are situations where brands have employed AR and VR to enhance the user experience.

• Ikea avails augmented reality to allow people to visualize how furniture will appear in their homes before purchase.
• L’Oréal offers a virtual makeup application that lets customers visualize how cosmetics appear on their faces.
• Lenskart allows consumers to test on glasses before purchasing virtually.

Artificial Intelligence and Machine Learning

Predictive analysis is rapidly entering the mobile application business. The mCommerce applications are already utilizing two key technologies, Artificial Intelligence (AI) and Machine Learning (ML), to collect the essential customer data that assists them in making sensible judgments.

It is a technology that automates data analytics, prediction, and decision-making processes. Apps may be made smarter with the use of artificial intelligence, hence enhancing their performance at every level. AI will transform app development from the backend development process to the user experience on the front end in 2023.

Apps for Wearable/Foldable Devices

Wearable and foldable gadgets constitute a new device segment in the app market. A significant number of applications will operate on foldable devices that run apps; thus, the growth of app development in this industry will be enormous.

While foldable devices offer the ease of giant screens on smartphones, wearables provide the convenience of running applications with the fingertips. As the sales of smartwatches continue to increase, so will this tendency.

Internet of Things (IoT)/Cloud App Integration

The internet has deeply penetrated our lives. We are surrounded by Internet-connected gadgets, ranging from smartphones, laptops, and tablets to voice-controlled smart home devices. In addition, cloud computing and IoT provide a substantial market for app development.

It is one of the most recent mobile app development trends. Even according to Cisco, at least 90% of mobile app traffic will be conducted in the cloud.

The industry will see an increase in cloud computing and Internet of Things-based applications (IoT). The advanced security these applications offer will encourage more developers to devote effort to them.

IoT and cloud-based applications will save operating expenses. Using APIs will enhance connections to platforms and improve efficiency.

Now, brands such as Samsung, Xiaomi, Bosch, and Honeywell are also rapidly embracing the Internet of Things technologies.

Blockchain Technology

Blockchain initially came to our attention during the bitcoin boom. However, technology has advanced significantly and is relevant in various contexts. Thanks to its rapid expansion, it is predicted to generate USD 20 billion in revenue by 2024. Blockchain also plays a crucial role in mobile app development.

Decentralized database-based applications will be in demand. One of the primary motivations for these applications is the security they provide. With this technology, sensitive data cannot be manipulated or stolen, as blockchain is exceptionally secure.

Some of the areas where blockchain apps will work more:

• Medical and healthcare
• Digital identity and passwords
• Voting systems
• Banking sector
• IoT and cloud computing

Many mobile applications will include decentralized applications, and blockchain developers will rise as a new trend.

On-Demand Apps

This generation of On Demand applications will be in great demand and on the rise in 2023. Examples of such successful applications are Airbnb and Uber, which have demonstrated the viability of apps in this market.

The market for On-demand mobile applications and websites will reach a staggering USD 335 billion by 2025, according to the most recent PwC estimate.

Annually, users spend USD 57.6 billion on on-demand services. Several industries will experience growth in the use of these applications, which will revolutionize the on-demand market:

• Virtual tutors and coaches
• Food delivery
• House cleaning
• Maintenance services
• Fitness on-demand
• Pet care
• Barber and beauty salon

Mobile App Development Beyond 2023

There are about 6.6 million Android and iOS apps available. It is expanding fast, and developers need to keep up with the technology to progress.

The trends in mobile app development services that are on the rise are not limited to those listed above. However, they are the ones that will be the most prominent.

The need for mobile app developers to utilize these technologies will inevitably expand; thus, it is prudent to start now.

• One of Onehouse’s lakehouse’s main benefits is that it eliminates the need for businesses to manage their extract, transform, and load (ETL) procedures, enabling users to begin studying data as soon as it is generated.
• The firm promised that the money from today’s round would continue developing Onehouse and extend its team to satisfy market demand for its product, so the new feature is probably the first of many to come.

Onehouse, a managed data lakehouse company, recently announced Onetable, a new feature for its platform, which will promote its vision of more straightforward, less expensive, and quicker data lakes.

It revealed the news as it disclosed that Addition and Greylock Partners, who had previously co-led its seed round in February 2022, has raised USD 25 million in a Series A round of fundraising on their behalf. Onehouse has so far raised USD 33 million.

Enterprises can use Onehouse’s data lakehouse, a relatively new service, to extract insights from their data. It has claimed to incorporate the best data lakes and warehouse elements into a single platform.

The business’s lakehouse service is a cloud-based, user-friendly offering. A lakehouse environment typically requires months of implementation effort and technical knowledge to set up. Still, Onehouse claims it can do away with all that and get its platform up and running in only a few short minutes.

The platform is built on the Apache Hudi open-source software developed by Vinoth Chandar, the founder and CEO of Onehouse, who also worked as a data architect at Uber Technologies Inc. at the same time. Each day, Apache Hudi is used by Uber to process 500 billion data points. Amazon.com Inc., Walmart Inc., and the aviation division of General Electric Co. are additional Apache Hudi users.

One of Onehouse’s lakehouse’s main benefits is that it eliminates the need for businesses to manage their extract, transform, and load (ETL) procedures, enabling users to begin studying data as soon as it is generated. It can access data from practically any data warehouse, including Google BigQuery, Snowflake, AWS Redshift, and data lake engines like AWS EMR and Databricks.

Companies struggle with ETL because it’s a labor-intensive procedure that frequently takes hours, which makes the data less usable by the time it’s evaluated. Data may be consumed practically immediately after it is created with Onehouse, enabling businesses to examine their data instantly.

Onehouse customers may now take advantage of native performance accelerations in systems like Databricks and Snowflake by collaborating with their corresponding open metadata layers, Delta Lake and Apache Iceberg, thanks to the advent of its new Onetable capability. Onetable avoids data fragmentation, according to the business, by doing away with the need to replicate data.

As a result, businesses can delegate data management to low-cost, open-source cloud warehouse services while streamlining their data architecture and supporting a more comprehensive range of data use cases. It includes traditional analytics, data science, stream processing, machine learning, and artificial intelligence.

Chandar said, “Over the past year we have built a first-of-its-kind cloud product to get data lakes up and running with just a few clicks. With Onetable, we are addressing a huge gap in the market around data interoperability, while enabling our customers to use Onehouse seamlessly with any major query engine.”

The firm promised that the money from the recent round would continue developing Onehouse and extend its team to satisfy market demand for its product, so the new feature is probably the first of many to come.

According to Jerry Chen of Greylock Partners, Onehouse is significantly influencing the data lakehouse industry. He said, “It’s delivering core infrastructure needs like data management, ingestion, performance tuning and interoperability with the ease of a cloud data warehouse.”

• The growth factors assisting the expansion of fintech include technological development, operational efficiencies, data infrastructure, a growing number of financial institutions, and the need for secure financial transactions.
• Fintech is catalyzing with startups funded on venture investments and existing firms offering digital services.

Technological incorporation in financial services and transactions has gained significant traction and impetus in the past few years and is continuing to penetrate future economic operations. Fintech (Financial Technology) automates financial deliveries, services, transactions, and other procedures.

Speaking of core functionalities, the technology helps financial institutions, enterprises, companies, and customers to track and monitor transactions with the aid of algorithms and customized software on computer systems or over smartphones.

It was earlier confined to operating the back-end systems of prominent financial institutions. Later, with many updates, the technology started housing more customer-oriented operations, branching into various domains and sectors such as wholesale and amp; retail, fundraising institutes, investment firms, and private and government banking services.

Previous Fintech Scenario

The total investment value of the fintech market during 2010–2019 hit USD 215.1B. Later, the figures were reported as USD 127.7B and USD 226.5B in 2020 and 2021, respectively. The global fintech market segmentation comprises payments and amp; transfers, savings and amp; investments, digital lending, online insurance, e-commerce, financing, etc.

Driving Factors

The adoption of digital payments during and post-COVID-19 pandemic surged the growth of the global fintech market. Several feasible options, such as cash displacement, digital currencies, request to pay, and Buy Now Pay Later (BNPL) services, have added to the market expansion. The US alone reported approximately 80% of the total global investment in the fintech sector.

Future Trends of Fintech

Developing economies such as India stand among the rapidly growing fintech markets, competing with the UK in funds and startups. The growth factors assisting the expansion of fintech include technological development, operational efficiencies, data infrastructure, a growing number of financial institutions, and the need for secure financial transactions.

Let’s dive deep into the Fintech trends that will boom in 2023 –

• Reduction in physical transactions: The global pandemic stoked the demand for secure and remote transaction options, paving the way for many fintech service providers. On a large scale, consumers have shifted their preference to online payouts from conventional physical methods. Micro, small, and medium enterprises have adopted fintech services for lucid transactions. Technology helps them distinguish between large corporations’ and individual consumers’ requirements.
• Eliminating the lags in payment and transactions: Instant and quicker processing has been an integral feature of the rising fintech sector. Irrespective of the amount sent or received, the back-end algorithms need to keep the time minimal and transactions transparent at both ends.
• Ecosystem banking: These advanced services help banks and other financial institutions to augment customer experience and maintain long-term value. Customers are offered a unique, uniform solution to avoid the prevalent, complex, and inconsistent processes running over various applications. The earlier expensive and tedious applications compelled banks to explore and introduce conducive services.
• Favorable investment platforms: For several decades, the population from developing countries preferred FDs, real estate, and physical assets as investment options, with minimal knowledge of capital market investments. The low number of demat accounts penetration has been indicative of this. However, the last two years featured remarkable growth in stock investments. The latest investment options, such as stocks, bonds, real estate, cryptocurrencies, NFTs, and others, are highly technology driven and promising. These investment operations thrive on technologies like mobile and amp; net banking applications, digital wallets, or blockchain.
• Regulatory compliances: The advanced authentication and verification techniques provided by fintech companies assist the authorities in curbing digital fraud and financial breaches. Although financial institutions operate with timely and accurate reporting, some incidences of poor coordination and incorrect KYC processes highlighted the loopholes in the services provided by several fintech companies, thus, creating the demand for stringent regulations.

Neobanking- Future of Fintech

Traditional banking with long queues, waiting time, determined working hours, and security issues cannot run in pace with modern-day technology-oriented operations and customers. To tackle these hindrances, an online new-age banking service emerged with no physical locations at all, called neobank.

It offers simple digital solutions for money transfers, payments, lending, and other transactions. The services include deposition and withdrawal, debit cards, investment facilities, credits, etc. Most neobanks collaborate with licensed banks to avail of financial services, as they do not have a banking license for standalone operations.

Due to no physical branches available, a hefty amount spent on infrastructure, rent, and electricity is considerably saved. Instead, this amount is channeled toward customers as lower maintenance fees and a higher interest rate. It is remarkable to note that despite having only an online presence, neobank houses more workforce and money resources.

Gone are the days when the account opening process in traditional banks was long and tiresome, with physical visits, forms, verification, and longing periods. With neobanks, things have turned fast-paced and digital. Accounts can now be created remotely over a computer or mobile phone, getting operational within a few minutes. Even the task of depositing cheques, which was earlier entirely confined to physical visits to the branch, is now feasible with the click of a device.

The User Interface (UI) of neobanking apps is simple, resolved, and aesthetically rich. To keep up with the technological allies, even the traditional brick-and-mortar banks launched their digital apps and offerings but somewhere lagged behind neobank’s interface due to technical glitches. Neobanks work with the best tech-savvy personnel to develop, maintain, and update the graphical interface.

The RBI does not regulate Neobanks in India; hence, they enjoy a little more autonomy than traditional banks. They can keep their maintenance and transaction charges low, as they are not subject to the regulations and policies of legacy banks. Neobanking is for technology-oriented and experimental professionals willing to sail over a growing fintech industry.

Though nascent, neobanks hold immense potential to revolutionize traditional financial services. Soon, neobanking is expected to become mainstream, with governments worldwide promoting the digital payment infrastructure.

Apart from banking services, neobanking is also finding applications in Small and Medium Enterprises (SMEs) business operations, that were initially dependent on traditional financial systems.

After switching to neobanks for financial processes, most enterprises observed and enjoyed significant benefits of the fintech world like –

• Instant payouts help them save time, need lower manual efforts, and eliminate discrepancies.
• Vendor payments are made with intelligent invoice generation, and all crucial accounting platforms can be smoothly integrated.
• Some of the top neobanks currently operational in the fintech sector are Fi Money, Jupiter, Freo, and RazorpayX.

Insurtech – A Promising Trend of Modern-day Fintech

Insurtech includes the technology curated to explore efficiency and cost savings from the traditional insurance model. Insurance products and services are priced more competitively with aid from Artificial Intelligence (AI) and data analysis. Besides, claims processing, risk evaluation, contract processing, and policy underwriting have become highly effective with insurtech.

Besides providing suitable pricing models, insurtech startups are also using deep learning-trained AI to perform broker’s tasks and generate an appropriate sync of policies for individual coverage. Moreover, spontaneous and on-demand insurance is provided for the events such as car borrowing, wherein customized coverage is offered with applicable rebates.

Insurtech has changed the way of coverage execution and payment. Users can access customized coverage online without branch visits or speaking to representatives. The advanced way of data collection and processing helps frame a consistent and reliable insurance policy that meets customers’ requirements. Instead of opting for a long-term locking period, users can also prefer premiums for a specific duration. Any fraudulent activity with data inconsistency can be immediately spotted with advanced data analytics and Machine Learning (ML). Even big data assists in detecting security loopholes to prevent insurers from losses and exploitation. The major functional areas of insurtech are claims management, contract execution, underwriting, and risk mitigation. The innovations keeping it onboard are AI/ML, automation, big data, blockchain, drones, and the Internet of Things (IoT). Major financial areas with fintech penetration are roboadvisors, investment apps, payment apps, personal finance apps, P2P lending platforms, crypto apps, etc. Significant insurtech companies dominating the market are Lemonade, Dacadoo, Bdeo, Etherisc, and Avinew, among others.

Fintech Market Landscape

Fintech is catalyzing with startups funded on venture investments and existing firms offering digital services. North America has been pioneering with the most fintech startups, followed by Asia and Europe. Broadly, the user categories of fintech include business clients, B2B for banks, B2C for small enterprises, and consumers. The growing advent of mobile banking, data analytics, and data decentralization will cater to more opportunities for all industries. The business owners used to visit banks to fetch finances or startup capital. To procure credit card payments, they had to establish a relationship with credit providers and install necessary infrastructure, including landline connections. With the advent of fintech services, these traditional obstacles are obsoleted.

Bottom Line

The advent of multiple financial software and evolving technological innovations are expected to drive the fintech market in 2023. Experts in the early developmental stage perceive the technology as promising to penetrate more domains and verticals. The three years post-global pandemic witnessed surging adoption of fintech services due to massive demand for remote transactions. In the later developments of the technology, it is anticipated that people would be using it for almost entire financial requirements, as it is becoming more flexible and user-friendly. E-commerce is going to enjoy surplus benefits such as increasing payment gateways, simplified personal and business transactions, credit provisions through apps and sites, portals with state-of-the-art interfaces, and an easy account setup process. Physical bank visits are going to decline significantly due to remote online assistance.

• The company claims that the data migration features on the Komprise platform can transport data to the cloud up to 27 times more quickly than rival alternatives.
• The company claims that businesses may more readily decide which kind of cloud storage infrastructure is ideal for their workloads by using its software.

Komprise Inc. has raised USD 37 million to expand its data management platform, which helps companies effectively move on-premises workloads to the cloud.

The recent investment was announced by Campbell, the California-based company. Top Tier Ventures, Celesta Capital, Canaan Partners, and Multiplier Capital invested. A total of USD 85 million has been raised.

Multiplier Capital Managing General Partner Kevin Sheehan said, “We invested in Komprise because of their impressive growth and path to profitability combined with the massive opportunity in edge data management and unstructured data for AI/ML in the cloud. We believe in the company’s market, vision, team, and execution.”

It can take a long time to transfer data from on-premises infrastructure to the cloud. The process takes longer as a corporation transfers more data. The company claims that the data migration features on the Komprise platform can transport data to the cloud up to 27 times more quickly than rival alternatives.

The platform’s performance is partly supported by a technology called Hypertransfer. It is intended to fix issues in the SMB protocol, a network technology businesses frequently use to move data to the cloud. According to Komprise, Hypertransfer expedites data transfers and enhances cybersecurity.

Using the SMB protocol, an on-premises server can transfer files to a cloud environment, but the cloud environment must reply with data proving it has received the files. Typically, this procedure is carried out repeatedly. File transfers are slowed since the cloud environment uses a lot of bandwidth to send data back to the server.

The amount of data traffic that the SMB protocol creates during file transfers is decreased by Komprise’s Hypertransfer technology. The business claims that, as a result, information can be transferred from on-premises environments to the cloud more quickly.

Hypertransfer transfers files via a proxy rather than loading them directly into the environment while moving data to a cloud environment. A server that serves as a middleman for data transfers is known as a proxy. This configuration, according to Hypertransfer, makes it harder for hackers to create network connections to a company’s cloud deployment.

The platform from Komprise claims to make other jobs more accessible. The performance and price of cloud companies’ various types of storage infrastructure differ. The company claims that businesses may more readily decide which kind of cloud storage infrastructure is ideal for their workloads by using its software.

Data transfer to the cloud frequently involves a sizable amount of physical labor. Komprise claims that its platform automates a large portion of the most time-consuming parts of the work. For instance, if a technical problem halts data transmissions, the platform can automatically restart them.

According to reports, Komprise intends to add various functionalities using the recently disclosed funding round, including the platform’s data migration functions. The startup will quicken go-to-market initiatives concurrently.

• Iguazio’s services assist companies in building training datasets for developing a neural network that can be immediately deployed for production.
• After the acquisition, McKinsey plans to incorporate Iguazio’s services into its QuantumBlack professional service unit.

McKinsey and amp; Co., a management consulting firm, announced the acquisition of Iguazio Ltd., a startup offering a platform for automating Artificial Intelligence (AI) development projects.

The financial terms of the deal are not disclosed. Iguazio reportedly amassed USD 72 million from Samsung Electronics Co. Ltd. and other investors.

McKinsey is one of the largest management consulting companies, with more than 30,000 employees across 60+ countries. The privately held company is said to garner annual revenue of more than ten billion dollars.

Iguazio, a Tel Aviv-based startup platform, sells software services to help companies build AI models more automatedly. Robert Bosch GmbH, NetApp Inc., and other huge enterprises are among the prominent customers of the company.

Ben Ellencweig, the Senior Partner of McKinsey, said, “After analyzing more than 1,000 AI companies worldwide, Iguazio was identified as the best fit to help us significantly accelerate our AI offering – from the initial concept to production, in a simplified, scalable and automated manner.”

Iguazio’s platform assists companies in building training datasets used to develop a neural network that can be immediately deployed to production. The AI development process has traditionally been a function of considerable manual work. The platform strives to automate most of the manual tasks for software staff.

Generally, the AI applications don’t evaluate the data ingested in its original form but simplify initially. A neural network that accepts product prices as input might round off the figure to the closest whole number before analysis. The streamlined data points obtained by this process are called features.

Every time an AI model receives new records, it should immediately turn them into features in real-time to prevent process strangling. However, attaining such real-time performance is practically challenging. Iguazio’s platform offers features that can simplify entrepreneurial tasks, thereby improving the AI applications’ performance.

Besides, the startup assures to address data drift, another crucial challenge the companies face. Data drift is a term used for a technical issue encountered by AI models when the data processed changes over time. A neural network trained to spot a technical glitch in specific industrial equipment may sometimes face data drift if it is remodeled for assessing error logs from different types of machines.

The accuracy of AI models often dwindles with the occurrences of such data drifts. Iguazio’s platform voluntarily redirects the AI model to resolve accuracy issues after detecting any data drift. It also creates dashboards for software teams to monitor the reliability of neural network alterations with time.

Besides the software platform, Iguazio offers two open-source AI development tools. The MLRun tool automates the task of integrating AI software code into software containers. The startup also develops Nuclio, which eases the data processing used in AI projects.

After the acquisition, McKinsey plans to incorporate Iguazio’s services into its QuantumBlack professional service unit. This integrated unit can help companies to build and instill AI models. QuantumBlack originated in 2015 after McKinsey acquired a London-based machine learning and analytics company with the same name.

• Systems employing artificial intelligence and machine learning Protect AI has exited stealth mode with USD 13.5 million in new investment and the release of its first product, NB Defense.
• It is estimated that there are currently over 10 million publicly available Jupyter Notebooks, with the number increasing by more than 2 million every year.

Protect AI, the Artificial Intelligence (AI) and Machine Learning (ML) systems cybersecurity startup, has exited stealth mode with raising USD 13.5 million in new investment and the release of its first product, NB Defense.

NB Defense is a free product that claims to be the first security solution in the market to address vulnerabilities in Jupyter Notebooks, a component utilized at the beginning of the ML supply chain. These web-based apps let developers generate and share documents with live code, equations, visualizations, and other data for coding purposes, such as data cleansing and transformation, statistical modeling, data visualization, and machine learning.

It is estimated that there are currently over 10 million publicly available Jupyter Notebooks, with the number increasing by more than 2 million every year. It is also suspected that there are several other Jupyter Notebooks installations in private repositories.

Protect AI was formed by a leadership team with AI business experience at Amazon Web Services Inc. and Oracle Corp., including co-founder and CEO Ian Swanson who was former AWS’s global head of AI and machine learning. Acrew Capital and boldstart ventures co-led the funding round, with Knollwood Capital, Pelion Ventures, and Avisio Ventures also participating.

Ian Swanson stated, “I have seen over 100,000 customers deploy AI/ML systems and realized they introduce a new and unique security threat surface that today’s cybersecurity solutions in the market do not address. This is why we founded Protect AI. ML developers and security teams need new tools, processes, and methods that secure their AI systems.”

Swanson noted that since virtually all ML code begins with a notebook, the business believed it to be the most natural starting point for accelerating a necessary industry shift.

Ian Swanson said, “We are launching a free product that helps usher in this new category of MLSecOps to build a safer AI-powered world, starting now. But many more innovations that will be released quickly across the entire ML supply chain.”

As MLOps has aided in accelerating the deployment of machine learning in production, the likelihood of security incidents has grown, and new vulnerabilities have been introduced into the enterprise machine learning supply chain. Examples of security vulnerabilities include Jupyter Notebooks incompatible with existing static code analyzers, tainted training data, arbitrary code execution in serialized models, and model evasion utilizing adversarial machine learning approaches.

NB Defense adds a layer of translation from existing security capabilities to enable scans of Jupyter Notebooks, then communicates results natively in the notebook or via reports with context-specific connections to problematic locations inside the notebook for rectification.

The offering examines a notebook for the standard Common Vulnerabilities and Exposures database in open-source ML frameworks, application tokens, libraries, and packages, and other credentials, and nonpermissive licensing in the frameworks.

NB Defense is currently accessible with a free license. Users can install NB Defense and utilize the JupyterLab Extension or Command Line Interface.