Home Enterprise bank How enterprise AI architecture is transforming every industry, from commerce to wealth management, and beyond

How enterprise AI architecture is transforming every industry, from commerce to wealth management, and beyond



Artificial intelligence (AI) holds great promise for solving problems in almost any industry. AI-powered and AI-powered technologies are now present and capable of automating tasks in retail and wealth management, to name a few. These automations reduce errors, manage ever-expanding data sets, and free humans to perform smart, strategic tasks. At the enterprise level, the AI ​​architecture is transforming capabilities and gradually shaping the way companies of the future operate.

Connection to basic systems of commercial enterprises

Operationalizing machine learning or AI at scale is a key priority for the world of retail and commerce. Enterprise technology stacks take advantage of AI and predictions for high frequency ambiguous situations. Active learning and continuous improvement of AI are built into business applications and workflows. Using these requires a contextual assembly of signals to create a unified view of truth, which enables teams to make contextual decisions in the present. While technology frameworks have been around for nearly a decade, most companies have been unable to overcome the hurdle of applying technology in real-world or large-scale contexts.

Most traders do not understand how to use tooling, and even enterprise level companies may not have the expertise to do so. The reality is that retail and commerce business leaders recognize that these tools exist and have value, but are prevented from realizing that value due to inflexible systems or workflows.

Trade won’t slow down, and no retailer in the world can shut down operations to add AI / ML or make better use of it. So the key struggle becomes, how to learn from machine learning that runs in production?

The potential of these systems makes the question urgent. For example: Data organized by machine learning and AI systems can give retailers the ability to predict whether or not someone will buy a particular product. Retailers can send an offer that will increase the likelihood of a purchase. The challenge is that conditions change, which means the model requires continuous adaptation and updating to solve new problems.

Hypersonix is a company that has built a backbone, with multiple artificial intelligence algorithms, through a combination of micro-services powered by a foundational business AI layer. Historically, companies have struggled to extract value from data because their data is organized in silos and difficult to access and analyze. It is extremely difficult for companies to strategize to get the right product to the right customer at the right price through the right channel.

Decisions about manufacturing, distribution, pricing, promotion, assortments to transport and offers to generate are made in information black holes often fueled only by instinct, static rules or, in the best of cases, cases, through historical analysis. The inability to combine historical analysis and forecasting future demand to make the best decision in the present is often the cause of lost opportunities and unmitigated business risks.

Overcoming the wide variety of data formats and structures, enterprise data silos, inconsistent decision-making processes, and archaic and fragile business systems, the Hypersonix team built a sophisticated two-step process.

Technical Director Kumar Srivastava explains it this way: “Our platform focuses on the entire world of commerce: from manufacturing to shipping, pricing and returns. It is extremely wide. We tackle the question of how big companies connect thousands of different signals so that the company can make better decisions about what to build, how to ship it, how to price, how to promote it when to take it out. traffic and more. What we fundamentally believe is that every business cares about delivering the right products to the right customers at the right time through the right channel.

This requires restructuring all business decisions so that they are fundamentally based on a demand forecast that predicts the demand for every product at every location at any given time. This is incredibly difficult, because not only do businesses have massive product catalogs, but also because every product in every location can have very different patterns, buying behaviors, and consumer preferences. To make matters even more complex, product catalogs, consumer preferences, product purchasing behaviors and local conditions are constantly changing, constantly changing.

Most companies are completely incapable of keeping up with such dynamic environments, often falling back on static rules or instinctive decisions. Hypersonix’s product and technology stack is designed to first bring together disparate data sets into a unified data model for commerce, and then to allow highly contextual and personalized algorithms to be executed at a very high frequency. high.

Business operations aren’t the only area where AI is supporting growth; people themselves, and especially their money, are also getting a boost.

Private banking for the people

As start-ups multiply and the number of high net worth individuals increases, innovation is developing in wealth management and the private banking sector. More … than 8% of American adults are millionaires, and this wealth is not segmented in the west. Asheesh Chanda spent over 15 years working in finance, but when he went to open a personal bank account, he found that even being a millionaire made him fail. Chanda acknowledged that there are a lot of people like him, who have similar amounts of personal wealth, but no opportunity for quality banking products or advice. They want a private bank account, but are stuck with the retail business.

Kristal is a platform that uses AI to democratize private banking. Chanda has discovered that an important aspect of the private banking experience is personalization. He noticed that the existing model of private banking was very human-dependent, and therefore had the inherent human weaknesses of bias and limited information.

Chanda describes the dilemma: “If you work with an advisor at a private bank, he will most likely refer to the funds he knows best and knows best. If you approach this person for personal portfolio recommendations, you will get an opinion shaped by that person’s personal experience. We felt that this human bias can be resolved using AI. AI can take large data sets and identify the best choice among a large number of products. “

Kristal’s innovators felt they could go beyond data analysis and interpretation, and developed a system to deliver standardized advice at scale. Chanda explains: “In a traditional model, your investment experience is limited to the advisor you are attached to, as well as their knowledge and understanding. With AI, you can choose the investment experience of a few people, merge it with AI, and deliver it to hundreds of thousands of investors. Large-scale personalized advice with very consistent quality. Different investors in different parts of the world experience the same quality without being limited by their personal experience. “

As the platform evolved, Kristal became much more than creating custom portfolios. The smart system can help people rebalance portfolios and provide one-click recommendations. Today, Kristal uses algo to make stock and ETF recommendations; customers can save time on research and use algo collation instead.

Especially after the 2008 crash, people are suspicious of humans. The switch to algo was reasonable, but the algo itself is also limited. For example, the algo can tell us that the markets have collapsed, but it cannot tell us why.

Kristal offers a hybrid model, leveraging the computing power of algo complemented by the investment experience of humans. This is an optimal blend of a consulting model, which is powered by AI. Chanda sums it up this way: “In the next 5 to 10 years, it will be the algos who will trade more in the market than the human traders themselves. Algos will play a more central role in making investment decisions on behalf of clients. It is natural that an advisory system also uses an algorithm. It will likely become a basic expectation for algos to be part of an advisory capacity. Just as a website and an app have become a given, we believe that in the private wealth management industry, algos will be part of any progressive counseling practice. “

Buying, selling, and trading are the three areas where AI is making systemic change, for the benefit of humans.

Driving digital transformation around the world

Across all industries, AI is changing the way business is done. Systems that can lighten manual processes, refine knowledge and accelerate progress are now in play. This opens up endless possibilities, which may increasingly be accessible not only to tech geniuses or billionaires, but also to people. ordinary people with a vision of positive impact.

At a high level, platforms like this drive innovation faster, help customers find new opportunities, and accelerate digital transformation around the world.



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