Tektonic AI raises $10M to build GenAI agents for automating business operations

One banking organization has used automation to apply a rule in the loan origination process that automatically rejects loans that fail to meet minimum requirements. This reduces employee workload and enables them to focus on the customers that will generate profit. Another European bank launched a strategic initiative to shrink its cost base and increase competitiveness through superior customer service. Upon completion of the first successful pilots, the bank’s automation program consisted of three phases. This bank then did some due diligence to determine whether there was a viable business case to automate each process within a reasonable time frame.

To start, thousands of cell cultures are tested and paired with images of the corresponding experiment. Using an off-the-shelf foundation model, researchers can cluster similar images more precisely than they can with traditional models, enabling them to select the most promising chemicals for further analysis during lead optimization. Generative AI tools can facilitate copy writing for marketing and sales, help brainstorm creative marketing ideas, expedite consumer research, and accelerate content analysis and creation.

Digital banking must now go well beyond the plain capabilities of displaying account credits and debits. In the digital age, customers expect their primary mode of engagement with banks to be digital, including a full set of value-added services. In addition to these new customer expectations, rising financial services regulations such as DORA (Digital Operational Resilience Act) continue to point the way for institutions to improve security and resilience and be adaptive to comply. Operations staff will have a very different set of tasks and thus will need different skills.

The processing of data through automated banking reduces such risks and errors to zero. Our latest survey results show changes in the roles that organizations are filling to support their AI ambitions. In the past year, organizations using AI most often hired data engineers, machine learning engineers, and Al data scientists—all roles that respondents commonly reported hiring in the previous survey.

Looking at the Inputs tab of our job template, we notice a set of template inputs called action, hostgroup_name, and insights_id. Those inputs are populated at runtime by the webhook template after parsing the triggered event. These inputs are then replaced in the template when generating the Ansible playbook that performs automation against Insights API. Note that the code used in the article is provided in GitHub to facilitate imports. Our job template is available in custom_automation_satellite_to_insights.erb file.

Automation’s Role in Bank Customer Service

Although our example shows simple operations synchronizing Red Hat Satellite and Red Hat Insights, the approach can be replicated to perform any other operational tasks that can be automated in your organization. Assuming all steps are configured correctly, each individual action should trigger the execution of our job template. You can monitor the launch and execution of the automation from Monitor and Jobs. You can also access the generated automation playbook that is executed for each job as this can be useful for troubleshooting. The import creates a new job template with the relevant automation code, as shown in Figure 4. We use data provided as part of the webhook event to drive operations tasks with Ansible.

Immersion customer assistance might find new directions with the combination of automation, augmented reality, and virtual reality. Automation would be able to access an increasingly wider range of data sources as 5G and the Internet of Things (IoT) developed, allowing for more responsiveness and customization in banking services. Instead of waiting on hold or being pinballed between different representatives, customers could get instant, efficient automated customer service powered by advanced AI. Cem’s hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection.

Many banks are rushing to deploy the latest automation technologies in the hope of delivering the next wave of productivity, cost savings, and improvement in customer experiences. While the results have been mixed thus far, McKinsey expects that early growing pains will ultimately give way to a transformation of banking, with outsized gains for the institutions that master the new capabilities. Aeologic Technologies stands at the forefront of this transformation, offering cutting-edge automation solutions tailored for the banking sector. Our expertise in AI, machine learning, and robotic process automation (RPA) enables us to design systems that streamline operations, enhance customer service, and ensure compliance with regulatory standards.

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Also, the time saved by sales representatives due to generative AI’s capabilities could be invested in higher-quality customer interactions, resulting in increased sales success. Generative AI has taken hold rapidly in marketing and sales functions, in which text-based communications and personalization at scale are driving forces. We estimate that applying generative AI to customer care functions could increase productivity at a value ranging from 30 to 45 percent of current function costs. Banking, high tech, and life sciences are among the industries that could see the biggest impact as a percentage of their revenues from generative AI.

From AI chatbots that handle basic inquiries to sophisticated algorithms that offer personalized financial advice, automation in banking is making customer interactions more efficient and productive. These technologies are not just transforming operations; they are redefining what is possible in retail banking. By adopting these automation solutions, banks can significantly improve their operational efficiency, enhance customer experience, and stay competitive in a rapidly evolving industry.

Automating the bank’s back office

Implementing automation allows you to operate legacy and new systems more resiliently by automating across your system infrastructure. But after verification, you also need to store these records in a database and link them with a new customer account. Cybersecurity is expensive but is also the #1 risk for global banks according to EY. The survey found that cyber controls are the top priority for boosting operation resilience according to 65% of Chief Risk Officers (CROs) who responded to the survey. The bank’s newsroom reported that a whopping 7 million Bank of America customers used Erica, its chatbot, for the first time during the pandemic.

In the first two examples, it serves as a virtual expert, while in the following two, it lends a hand as a virtual collaborator. In 2012, the McKinsey Global Institute (MGI) estimated that knowledge workers spent about a fifth of their time, or one day each work week, searching for and gathering information. If generative AI could take on such tasks, increasing the efficiency and effectiveness of the workers doing them, the benefits would be huge.

This in turn reduces employee workloads, helping them to feel more fulfilled and productive as they are equipped with the data and the time they need to provide the best possible experience for customers. Increasing customer expectations, stringent regulations and heightened competition are making it more important than ever for banks to optimize and modernize their operations. Automation is helping banks worldwide adapt to organizational and economic changes to reduce risk and deliver innovative customer experiences. With these six building blocks in place, banks can evaluate the potential value in each business and function, from capital markets and retail banking to finance, HR, and operations.

Armed with deep banking system knowledge, empathy, and strong communication skills, they provide personalized “white glove” service, aiming to redefine customer service in the industry. Today’s operations employees are unlikely to recognize their future counterparts. Roles that previously toiled in obscurity and without interaction with customers will now be intensely focused on customer needs, doing critical outreach.

The online survey was in the field April 11 to 21, 2023, and garnered responses from 1,684 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 913 said their organizations had adopted AI in at least one function and were asked questions about their organizations’ AI use. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP. Organizations continue to see returns in the business areas in which they are using AI, and

they plan to increase investment in the years ahead. We see a majority of respondents reporting AI-related revenue increases within each business function using AI. And looking ahead, more than two-thirds expect their organizations to increase their AI investment over the next three years.

At that time, we estimated that workers spent half of their time on activities that had the potential to be automated by adapting technology that existed at that time, or what we call technical automation potential. We also modeled a range of potential scenarios for the pace at which these technologies could be adopted and affect work activities throughout the global economy. In the life sciences industry, generative AI is poised to make significant contributions to drug discovery and development. Management automation encompasses various administrative tasks and processes related to the configuration, maintenance, and optimization of data center resources. This includes tasks such as software patching, configuration management, compliance auditing, and policy enforcement.

These efforts have delivered tangible benefits over the last five years, but often in isolated pockets, and without dramatically reducing overall operations costs. For example, one bank achieved a 20 percent efficiency improvement by applying lean in its account-closure process; a good result, but the process constituted less than 1 percent of the bank’s total operations cost and so did not move the needle. Another bank used smart workflow tools to automate corporate-credit assessments, improving productivity by 80 percent.

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In a survey, 91% of financial professionals confirmed the increase in fraud at their organizations year-over-year. By implementing an RPA-enabled fraud detection system, you can automate transaction monitoring to identify patterns, trends, or anomalies, preventing fraud. Since little to no manual effort is involved in an automated system, your operations will almost always run error-free. For example, a sales rep might want to grow by exploring new sales techniques and planning campaigns. They can focus on these tasks once you automate processes like preparing quotes and sales reports.

They have become the digital version of customer support and emerged as a new way to interact, offering personalized, prompt and efficient assistance on the text and voice-based channels of their choice. 52% of customers feel banking is not fun, and 48% consider that their banking relationships are not meshing well with their daily lives. A few customers also mentioned that their banks are missing the mark on providing seamless experiences, the kind of personalization they want, and cutting-edge innovation. This is a wake-up call for banks to step up their game with automation technologies.

RPA combines robotic automation with artificial intelligence (AI) to automate human activities  for banking, this could include data entry or basic customer service communication. RPA has revolutionized the banking industry by enabling banks to complete back-end tasks more accurately and efficiently without completely overhauling existing operating systems. Banks and financial institutions are harnessing these technologies to provide instant, accurate responses to a multitude of customer queries day and night. These AI-driven chatbots act as personal bankers at customers’ fingertips, ready to handle everything seamlessly, from account inquiries to financial advice. They’re transforming banking into a more responsive, customer-centric service, where every interaction is tailored to individual needs, making the banking experience more intuitive, convenient, and human. Customer onboarding in banking has taken a leap forward with AI-powered automation and chatbots.

Respondents at these organizations are over three times more likely than others to say their organizations will reskill more than 30 percent of their workforces over the next three years as a result of AI adoption. Generative AI could still be described as skill-biased technological change, but with a different, perhaps more granular, description of skills that are more likely to be replaced than complemented by the activities that machines can do. Researchers start by mapping the patient cohort’s clinical events and medical histories—including potential diagnoses, prescribed medications, and performed procedures—from real-world data. Using foundation models, researchers can quantify clinical events, establish relationships, and measure the similarity between the patient cohort and evidence-backed indications. The result is a short list of indications that have a better probability of success in clinical trials because they can be more accurately matched to appropriate patient groups. In the lead identification stage of drug development, scientists can use foundation models to automate the preliminary screening of chemicals in the search for those that will produce specific effects on drug targets.

Specifically, this year, we updated our assessments of technology’s performance in cognitive, language, and social and emotional capabilities based on a survey of generative AI experts. While we have estimated the potential direct impacts of generative AI on the R&D function, we did not attempt to estimate the technology’s potential to create entirely novel product categories. These are the types of innovations that can produce step changes not only in the performance of individual companies but in economic growth overall. Generative AI’s potential in R&D is perhaps less well recognized than its potential in other business functions.

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Choose an automation software that easily integrates with all of the third-party applications, systems, and data. In the industry, the banking systems are built from multiple back-end systems that work together to bring out desired results. Hence, automation software must seamlessly integrate with multiple other networks.

It has already expanded the possibilities of what AI overall can achieve (see sidebar “How we estimated the value potential of generative AI use cases”). Our second lens complements the first by analyzing generative AI’s potential impact on the work activities required in some 850 occupations. This enables Chat GPT us to estimate how the current capabilities of generative AI could affect labor productivity across all work currently done by the global workforce. But a full realization of the technology’s benefits will take time, and leaders in business and society still have considerable challenges to address.

The latest generative AI applications can perform a range of routine tasks, such as the reorganization and classification of data. But it is their ability to write text, compose music, and create digital art that has garnered headlines and persuaded consumers and households to experiment on their own. As a result, a broader set of stakeholders are grappling with generative AI’s impact on business and society but without much context to help them make sense of it. Note that the webhook template is conditioned based on the event name (including hostgroup_ and host_). The payload is populated according to the need of the job template automation configured earlier. This allows to grab and populate hostgroup and host related parameters (e.g. hostgroup_name and insights_id).

Its ability to rapidly digest mountains of data and draw conclusions from it enables the technology to offer insights and options that can dramatically enhance knowledge work. This can significantly speed up the process of developing a product and allow employees to devote more time to higher-impact tasks. Some of this impact will overlap with cost reductions in the use case analysis described above, which we assume are the result of improved labor productivity.

AI chatbots work with unparalleled speed and efficiency, handling tasks like data entry, transaction processing, and customer queries much faster than humans, increasing overall operational efficiency in the bank. Not just this, today’s advanced chatbots can handle numerous conversations simultaneously, and in most global languages and dialects. https://chat.openai.com/ Through data analysis and machine learning, AI chatbots offer personalized banking experiences. They remember customer preferences, suggest relevant products, and provide tailored advice, making each interaction unique and meaningful. The finance and banking industries rely on a variety of business processes ideal for automation.

As an example of how this might play out in a specific occupation, consider postsecondary English language and literature teachers, whose detailed work activities include preparing tests and evaluating student work. With generative AI’s enhanced natural-language capabilities, more of these activities could be done by machines, perhaps initially to create a first draft that is edited by teachers but perhaps eventually with far less human editing required. This could free up time for these teachers to spend more time on other work activities, such as guiding class discussions or tutoring students who need extra assistance.

This can be a significant challenge for banks to comply with all the regulations. On the contrary, RPA can help your bank resolve customer support challenges as the bots can work round the clock. Besides automating routine queries and responses, RPA can ensure accuracy and consistency, maintaining historical context to solve complex queries. It takes about 35 to 40 days for a bank or finance institution to close a loan with traditional methods. Carrying out collecting, formatting, and verifying the documents, background verification, and manually performing KYC checks require significant time.

Such applications can have human-like conversations about products in ways that can increase customer satisfaction, traffic, and brand loyalty. Generative AI offers retailers and CPG companies many opportunities to cross-sell and upsell, collect insights to improve product offerings, and increase their customer base, revenue opportunities, and overall marketing ROI. Faced with these challenges, few banks have had the appetite for reengineering their operations-related IT systems.

According to a 2023 Capco study, 37% of banking customers favor chatbots or text messaging in emergencies, while 63% prefer direct conversations with bank professionals. Automation reinforces the bank-customer relationship, ensuring staff availability for crucial situations. Banks have a unique opportunity to lay the groundwork now to provide personalized, distinctive, and advice-focused value to customers. June 20, 2019Today, deep within the headquarters and regional offices of banks, people do jobs that no customer ever sees but without which a bank could not function. Thousands of people handle the closing and fulfillment of loans, the processing of payments, and the resolution of customer disputes.

These innovations were made possible by leveraging expertise in bank procedures. Automating these and other processes will reduce human bias in decision-making and lower errors to almost zero. This will give operations employees time to help customers with complex, large, or sensitive issues that can’t be addressed through automation. And these employees will have the decision-making authority and skills quickly resolve customer issues. As genAI tools evolve, banking leaders and customers will encounter a growing number of applications and  risks. While genAI is poised to transform the banking industry landscape, proper governance is key to unlocking the technology’s potential.

This proactive approach demonstrates the bank’s dedication to addressing consumer concerns promptly while maintaining security and trust. Automation thus becomes a vital tool in delivering personalized and responsive financial services in 2023. The future of banking in 2023 revolves around optimizing workspaces, electrifying customer experiences, and responding to real-time demands. End-to-end service automation remains indispensable in navigating the dynamic financial landscape. The use of predictive analytics can dramatically improve the management of operations in several ways. First, it enables operations leaders to be more precise and accurate in their predictions.

To capture this opportunity, banks must take a strategic, rather than tactical, approach. In some cases, they will need to design new processes that are optimized for automated/AI work, rather than for people, and couple specialized domain expertise from vendors with in-house capabilities to automate and bolt in a new way of working. The Bank of America wanted to enhance customer experience and efficiency without sacrificing quality and security. However, AI-powered robotic process automation emerged as the best solution to overcome these challenges. Banks deal with a multitude of repetitive tasks, from data entry and transaction processing to compliance checks and customer support inquiries.

In fact, over the last eight years, these banks have managed to reduce their costs more than those that have been slower to embark on their journey to a digital operating model. Some banks are experimenting with rapid-automation approaches and achieving promising results. These trials have proved that automating end-to-end processes, which used to take 12 to 18 months or more, is doable in 6 months, and with half the investment typically required.

When banks, credit unions, and other financial institutions use automation to enhance core business processes, it’s referred to as banking automation. As more digital payment and finance companies emerge, making it easy to move money with just a click, traditional banks are struggling to keep up with these advanced services. Automation offers personalized and faster service, 24/7 customer support through chatbots, and streamlined processes for loan approvals and account openings, significantly enhancing the overall customer experience. As automation incorporates more AI and machine learning technologies, security and compliance with regulatory standards become increasingly complex.

Successful large-scale automation programs need much more than a few successful pilots. They require a deep understanding of where value originates when processes are IT enabled; careful design of the high-level target operating model and IT architecture; and a concrete plan of attack, supported by a business case for investment. This high degree of manual processing is costly and slow, and it can lead to inconsistent results and a high error rate. IT offers solutions that can rescue these back-office procedures from needless expense and errors. Our experience in the banking industry makes it easy for us to ensure compliance and build competitive solutions using cutting-edge technology. To overcome these challenges, Kody Technolab helps banks with tailored RPA solutions and offers experienced Fintech developers for hire.

Automating routine tasks and leveraging IoT for real-time monitoring and maintenance of banking infrastructure can significantly reduce operational costs and improve efficiency. For instance, IoT sensors can predict equipment failures or maintenance needs in bank branches, reducing downtime and maintenance costs. IoT and automation together can enhance security measures and fraud detection capabilities.

Our research indicates that a significant opportunity exists to increase the levels of automation in back offices. By reworking their IT architecture, banks can have much smaller operational units run value-adding tasks, including complex processes, such as deal origination, and activities that require human intervention, such as financial reviews. ​​Banking automation, spearheaded by AI and AI chatbots, has emerged as a game-changer in personalizing customer interactions, optimizing operational efficiency, and fostering a more inclusive and global banking environment. From simplifying customer onboarding to enhancing fraud detection and improving employee experiences, the impact of these technologies is profound and multifaceted.

By making faster and smarter decisions, you’ll be able to respond to customers’ fast-evolving needs with speed and precision. With cloud computing, you can start cybersecurity automation with a few priority accounts and scale over time. For the best chance of success, start your technological transition in areas less adverse to change. Employees in that area should be eager for the change, or at least open-minded. It also helps avoid customer-facing processes until you’ve thoroughly tested the technology and decided to roll it out or expand its use. With Aeologic, embark on a journey towards a more efficient, secure, and customer-centric banking future.

Timely reminders on deadlines and overdue will be automatically sent to your workforce. Customized notifications by the workflow software should be linked, and automatically to all common tasks. Your choice of automation tool must offer you fraud-proof data security and control features. Always choose an automation software that allows you to generate visual forms with just drag-and-drop action that will help further the business.

Red Hat Satellite is an infrastructure management tool designed for the management and operations of Red Hat Enterprise Linux (RHEL) environments. It allows system administrators to scale the management of their datacenters to thousands of hosts at ease, while implementing and enforcing secure and compliant standard operating environments (SOE). Enable any employee to work anywhere, anytime with seamless employee experiences. Strategic, Technical, and Future alignment gives you the best possible data center experience today and tomorrow. AccountsIQ, a Dublin-founded accounting technology company, has raised $65 million to build “the finance function of the future” for midsized companies. On the technical side, Tektonic utilizes a combination of foundation models and open models for entity extraction and lower-level actions.

• They can recognize suspicious patterns faster than humans, adding an extra layer of security to protect sensitive customer data and financial transactions.
• Armed with deep banking system knowledge, empathy, and strong communication skills, they provide personalized “white glove” service, aiming to redefine customer service in the industry.
• We can now configure the Ansible automation in Satellite that is going to be launched when an event triggers.

For example, customers should be able to open a bank account fast once they submit the documents. Using automation to create a cybersecurity framework and identity protection protocols can help differentiate your bank and potentially increase revenue. You can get more business from high-value individual accounts and accounts of large companies that expect banks to have a top-notch security framework. Robotic process automation, or RPA, is a technology that performs actions generally performed by humans manually or with digital tools. As RPA and other automation software improve business processes, job roles will change.

Foundation models have enabled new capabilities and vastly improved existing ones across a broad range of modalities, including images, video, audio, and computer code. AI trained on these models can perform several functions; it can classify, edit, summarize, answer questions, and draft new content, among other tasks. This article described how Red Hat Satellite events, webhooks and job templates can constitute a real platform for automating management operations.

Unfortunately, these teams often work in silos, leading to customer frustration from being shuffled between staff members. Chatbots in the front office and anti-payments fraud detection in the middle office were two of the most developed and extensively used AI use cases that had already become prominent in banks’ operations. Helps transform banks and non-banks across a broad range of topics to sustainably drive revenue growth and to enhance efficiency. The language of the paper have benefited from the academic editing services supplied by Eric Francis to improve the grammar and readability.

That’s thanks in part to cloud-based AI/ML solutions and APIs that can be orchestrated quickly to build powerful solutions. A few years ago, we helped a leading commercial bank streamline its underwriting process. The solution, which took 15 months to implement, scanned thousands of financial statements in varying formats and inputted them into a spreading credit application. It delivered a 40% improvement in efficiency and a 70% reduction in processing time. Automating compliance procedures allows banks to ensure that specified requirements are being met every time and share and analyze data easily. This frees compliance departments to focus on creating a culture of compliance across the organization.

Our team of experts can assist your bank in leveraging automation to overcome resource constraints and cost pressures. Through Natural Language Processing (NLP) and AI-driven bots, RPA enables personalized customer interactions. Chatbots can provide tailored recommendations, answer inquiries promptly, and resolve customer issues efficiently. Whether your bank experiences surges in workload during peak automation in banking operations periods or needs to streamline operations during quieter times, RPA can adapt to the changing demands of your business. Stiff competition from emerging Fintechs, ensuring compliance with evolving regulations while meeting customer expectations, all at once is overwhelming the banks in the USA. Besides, failure to balance these demands can hinder a bank’s growth and jeopardize its very existence.

Our expert team is ready to tackle your challenges, from streamlining processes to scaling your tech. In case of any fraud or inactivity, accounts can be easily closed with timely set reminders and to send approval requests to managers. Manual engagement with the financing and discounting requests can be an impediment to finance related to trading. From the payment of goods to the delivery there is a lot of documentation and risks involved. Implementation of automation can reduce the communication gap between supply chains and effectively ensure the flow of requests, documents, cash, etc.

You can foun additiona information about ai customer service and artificial intelligence and NLP. Yes, AI-driven systems analyze transaction patterns in real time to detect and prevent fraudulent activities, enhancing the security of customer assets and the banking environment. Banks must ensure that automation solutions are scalable and flexible enough to adapt to changing business needs and technological advancements. Choosing the right technology consulting services and platforms that can grow and evolve with the bank is crucial to achieving long-term success.

A global bank reinvented its auto loans process–boosting car loan sales by 50% and cutting total costs. Organizations that achieve a high level of maturity become “future-ready.” They are fully focused on digital transformation (i.e. Digital Focused) and gain the agility and resilience needed to thrive amid uncertainty. They also—probably as a result—realize higher market valuations and derive more profit.

Additionally, collaborations between data scientists and finance professionals will further ensure proper AI governance. The new DORA regulation, passed in 2022 but to come into force in 2024, at the latest, obliges financial companies to ensure the resilience of their operations with customers, with particular focus on vendor risk management and cyber risk. The new DORA framework does not only affect large banks, but applies to all types of financial companies, from credit and payment providers to investment and insurance companies, cryptocurrency exchanges and crowdfunding platforms. Banking automation is applied with the goals of increasing productivity, reducing costs and improving customer and employee experiences – all of which help banks stay ahead of the competition and win and retain customers. Banks have enhanced many of their customer-facing, front-end operations with digital solutions. Online banking, for example, offers consumers enormous convenience, and the rise of mobile payments is slowly eliminating the need for cash.

For example, generative AI’s ability to personalize offerings could optimize marketing and sales activities already handled by existing AI solutions. Similarly, generative AI tools excel at data management and could support existing AI-driven pricing tools. Applying generative AI to such activities could be a step toward integrating applications across a full enterprise. No one knows what the future of banking automation holds, but we can make some general guesses.

McKinsey predicts a future where automation and AI could handle 10 to 25% of tasks across bank functions, significantly freeing up human employees for more strategic roles. This not only boosts productivity but also enriches job satisfaction by removing mundane tasks from the daily workload. Being in the financial sector, banks are most required to be conscious and attentive about the data that they handle.