Invented by Pablo Tapia, Tupl Inc

The Market For Artificial Intelligence-Based Advisor Network

The market for Artificial intelligence-based advisor networks is on the rise, and its prospects look promising. AI helps wealth managers gain insight into their clients’ needs and preferences, allowing them to tailor portfolio recommendations tailored specifically for them.

Additionally, AI can speed up client onboarding and portfolio management processes. However, implementing AI at scale presents some difficulties.

Market Size

The Artificial intelligence-based advisor network market is expected to experience significant growth over the coming years due to an increasing demand for smart solutions. Numerous sectors, such as retail, healthcare, law and automotive & transportation can see benefits from these systems.

Over the last few years, many businesses have adopted AI and machine learning-based technology. Common applications include data analytics, predictive modeling, automation, and machine learning. Many industries have integrated these solutions into their operations to streamline processes, reduce costs, and provide superior customer experiences.

These industries are taking advantage of these technologies to boost operational efficiency and create new revenue streams. Retail firms, for instance, use AI-based recommendation platforms to offer customized products and services to their customers. These systems can recognize consumers’ shopping patterns and suggest items they might be interested in purchasing.

Similarly, the healthcare industry is taking advantage of AI to enhance patient care. AI-based medical imaging systems are becoming more commonplace, with companies like GE Healthcare investing in them for increased accuracy and speed.

AI also finds application in the security sector, where it can be employed to detect and respond to cyber attacks and malware. Organizations can benefit by protecting their clients and sensitive information while upholding privacy standards.

With the rise of AI-driven tools in financial advisory, many financial advisors now rely on this technology for greater insight into their prospects and clients. Furthermore, this insight allows them to prioritize and enhance their prospecting efforts, engage clients more effectively, and strategically construct growth campaigns for maximum success.

However, the use of these technology platforms can present a number of challenges. For instance, it can be challenging to ensure they comply with regulations and are user-friendly for customers and staff members. Furthermore, integrating the solutions into existing systems may prove challenging when deployed across multiple business units and regions. Ultimately, when developing an AI-based advisor network solution it is essential to take into account all stakeholders’ needs.

Market Share

The market for Artificial intelligence-based advisor networks is expected to experience rapid growth during the forecast period. This sector’s share of the overall IT market is predicted to increase due to increasing adoption of AI technologies across various industry verticals such as advertising/media, financial services, healthcare/life sciences, automotive/manufacturing and others.

The growing application of AI and machine learning (ML) in healthcare, such as robot-assisted surgery, dosage error reduction, virtual nursing assistants, clinical trial participant identifiers, hospital workflow management, and preliminary diagnosis is propelling the market for Artificial Intelligence-based advisor networks. These solutions assist physicians in designing tailored treatment regimens while cutting overall costs.

Furthermore, the application of these tools in the insurance sector is expected to open up vast opportunities for market expansion. Moreover, an expanding number of insurance agents worldwide is fuelling this segment’s expansion.

Another area of opportunity in the healthcare AI market is how AI and machine learning (ML) are being utilized to assist patients manage their health. With an increasing number of people living with chronic illnesses, healthcare organizations are utilizing ML algorithms to enhance patient care and reduce costs by automating key medical processes like drug discovery or clinical trial initiation.

For instance, healthcare companies that use AI tools to identify heart disease risks among patients can recommend personalized treatment plans and monitor progress in real-time. This allows doctors to offer tailored strategies, reduce healthcare expenses, and enhance patient outcomes.

However, there are challenges associated with integrating AI and ML into the healthcare system. State and federal regulations restricting their use may impede organizations from implementing them without jeopardizing patient privacy and security.

Additionally, the increasing adoption of cloud-based technologies and AI platforms for data management is another driving force in the market for artificial intelligence-based advisor networks. These platforms offer advanced technology that simplifies processes and allows businesses to focus on core operations.

Market Trends

The market for artificial intelligence-based advisor networks is expected to experience rapid growth over the forecast period. This growth is primarily fueled by factors such as rising popularity of AI in financial services, increasing adoption of machine learning and artificial intelligence technology, technological advancements, and favorable government initiatives to promote artificial intelligence.

To create an effective AI system, wealth management companies must pay careful consideration to selecting algorithms, collecting data and developing AI models. Furthermore, proper governance and controls are essential for long-term success.

When implementing AI systems, firms must be cautious to avoid any potential breaches or misrepresentations that could cause reputational damage and negatively affect the business. These missteps could potentially tarnish a firm’s image as well as financial loss.

Furthermore, firms must be able to explain the decision-making process when using AI systems and communicate with clients about the outcomes generated by the program. Doing this helps bridge the gap between front office and back office operations.

The financial sector is expected to accelerate the adoption of AI technology due to more accessible data storage, access to large databases and advancements in software and hardware. A recent McKinsey survey indicated that private investment in AI will rise over the next three years, providing new comprehensive solutions and tools.

According to estimates, AI will become increasingly prevalent in healthcare as patients share their medical histories with doctors and develop personalized treatment plans. Furthermore, AI has the potential to enhance patient experiences while cutting healthcare costs.

With the advancement of AI, many technologies are being created that can enhance customer service, personalize content and analyze consumer behavior. Examples include chatbots, AI-based analytics and digital assistants that answer questions, send notifications and offer feedback.

AI is also being utilized to bolster marketing and sales initiatives. It helps marketers identify specific groups of customers, so they can create more tailored content and experiences tailored to each group. Furthermore, it tracks and measures which messages are opened and shared, as well as enabling A/B testing for more precise targeting with pertinent messaging and materials.

Market Forecast

The market for Artificial intelligence-based advisor networks is projected to experience a strong compound annual growth rate (CAGR) during the forecast period. This expansion is mainly driven by increasing applications of AI across various industries, such as automotive, healthcare, retail & eCommerce, banking & finance and manufacturing.

AI technology has enabled the creation of new products and services such as virtual financial planning (VFP) and robo-advisors. These tools aid financial advisory firms automate many backoffice tasks while cutting operational expenses. This reduces expenses while freeing up human advisors’ time to interact more directly with clients.

These systems also present the opportunity to build AI-enabled chatbots that enable customers to communicate with advisors via messaging. Utilizing these chatbots can enhance engagement and client satisfaction by offering a more tailored experience.

Furthermore, AI has enabled financial advisory firms to develop more specialized products tailored towards specific investor preferences. For instance, a client with environmental worries can be served by an advisor who has an intimate knowledge of their issues and is equipped with solutions tailored towards their requirements.

Investment management firms can benefit from improved customer experiences and higher loyalty levels, which in turn helps drive their profitability and enhance their value propositions.

Financial advisory firms must remain competitive by adopting technological advancements and changing investor preferences. This requires improving their analytical capacity through AI technology and other innovations, while optimizing operational efficiency.

However, AI is a complex issue that necessitates specialist knowledge and specialized infrastructure. This makes it challenging for smaller firms to adopt and implement AI effectively. Thus, advisors should collaborate with industry leaders who possess both expertise and infrastructure in order to successfully execute successful AI initiatives.

North America led the global Artificial intelligence-based advisor network market in 2021 and is expected to remain at this position throughout the forecast period. This region boasts significant investment in digital technology as well as supportive government regulations that encourage AI adoption.

The Tupl Inc invention works as follows

A network fix application can automatically identify the root cause of an issue in a wireless carrier network. It will then generate a network fix priority to implement the solution before receiving a customer ticket or trouble ticket. A data adaptor platform might initially receive performance data from multiple sources about the user device and network components of a wireless network. Based on the performance data, the network fix application might analyze the performance data and use a trained machine-learning model to determine the root cause of the issue that is affecting one or more users. The network fix application can also analyze the performance data with another trained machine-learning model to determine a priority for network fix to resolve each root cause in the best order.

Background for Artificial intelligence-based advisor network

Wireless telecommunications carriers must manage and resolve wireless communication device and network problems in order to offer quality service to their subscribers. Many wireless telecommunications carriers use key performance indicators (or other types of network performance data) to evaluate the performance of their carrier network and address any problems. If you receive a trouble ticket regarding call quality (e.g. dropped calls, excessive power clipping or a combination thereof), additional resources can be provided per user to address the problem. This could include adjusting the size, shape, power levels, antenna tilt, and so on. These approaches may have significant lead-time delays, which can reduce customer experience and result in capital expenditures and operating expenses.

In some cases, network engineers may spend too much time processing and analysing trouble tickets due to a lack in resources or the proper tools. This is because the troubleshooting process is more reactive than proactive. It can lead to a delay in solving quality service issues which could result in revenue loss, customer base loss, and a decrease in business reputation.

This disclosure relates to methods for using a data-adaptor platform with a network fixing application to perform proactive analysis of network performance data and user device performance data to identify root causes of short- and long-term network problems. Prioritize network fixes to ensure a fast and efficient solution. Multiple data sources may provide the user device performance data as well as network performance data. Multiple data sources can provide RAN Operation Support System counters, Call Detail Records, (CDRs), alarm, alert, and trouble ticket data. This includes customer ticket data and network tickets data.

The data adaptor platform allows you to combine data from all the sources and perform real-time analysis to find areas with performance data below a certain threshold. The performance data is used to identify issues that negatively affect the performance of network components and user devices. This data is then analyzed by one or more machine learning models to determine the root cause of each quality of service issue. To prioritize a network fix, the issues can be further analysed using one or more machine learning models. This is done based on factors such as network performance and top offenders cells within the wireless carrier network.

In certain embodiments, a network fixing application can monitor performance data from user devices and network component in a wireless carrier network to determine if one or more geographical areas has a negatively affected performance or falls below a predetermined threshold. The network fix application can then analyze the symptoms and predict the root cause of quality service issues. The network fix application can recommend one or more actions to address quality of service issues. It may also prioritize a network fix for each issue based on the expected impact, duration, effect, available resources, etc.

The network repair application may use at least one trained machine-learning model to analyze user device performance data and network performance data in order to predict root causes of quality service issues and prioritize network fix. You can augment the machine learning model by adding training data sets or training results from another machine learning algorithm. This is based on feedback about the accuracy of root cause predictions and network fix optimization prioritization optimization.

Proactive analysis of user device performance data, and network performance data of a wireless network can help streamline the process for network maintenance by predicting root causes based on performance data and prioritizing network fixes with a foresight perspective. This will reduce the burden of dealing with subsequent subscriber trouble tickets or network trouble tickets. The machine learning model may also be used to automatically diagnose and resolve network problems in a timely manner. There are many ways to implement the techniques described in this article. Below are examples of implementations, with reference to the FIGS. 1-6.

Example Architecture

FIG. “FIG. Architecture 100 includes a data adaptor plate 116, a network fixing application 118, as well as an artificial intelligence module 122. The network fix application 118 and the data adaptor platform116 may be executed on one or more computing devices 126. The computing nodes may be distributed processing nodes that can scale according to workload demand. The computing nodes 126 can include general-purpose computers such as laptops, tablets, desktops, and servers. In other embodiments, however, the computing nodes may take the form of virtual machines such as virtual private servers (VPS) or virtual engines (VE). The computing nodes may store data in a distributed storage network, where data can be kept for extended periods of time and replicated to ensure reliability. The computing nodes 126 can provide processing redundancy and data storage, and may scale data processing and storage in response to changing demand. In a networked deployment new computing nodes may be added without affecting operational integrity of the data platform 116, network fix application 118, or artificial intelligence module 122.

The data adaptor platform (116) may contain a cloud layer that manages hardware resources, and a data management layers that manages data processing as well as storage. The cloud layer can provide software utilities to manage computing and storage resources. The cloud layer can provide a user interface that allows for the management of multiple storage services, such as local servers, Amazon AWS, Digital Ocean, and others. The cloud layer stores call data that has been collected using the data adaptor platform. 116 The cloud layer can also offer an integrated view of multiple servers or clusters from different providers such as Hortonworks? Cloudera? MapR?, MapR?, and others. The cloud layer can also provide monitoring utilities that allow you to monitor the utilization of resources as well as alerts for managing storage and processing capacity. The cloud layer can facilitate deployment, configuration and activation local and cloud servers as well as the deployment, configuration and activation applications and/or services.

The data management layer can include databases and software utilities that allow for the acquisition, processing and storage of data from multiple sources. The data management layer can provide an interface that allows for the use of different data storage locations. These data stores could include Hadoop Distributed File System (HDFS), Apache Spark?, Apache HBase? and/or so forth. (HDFS), Apache Spark? or Apache HBase? and/or other similar data stores. Custom analytic engines, third-party tools and other tools may use the APIs of the data storage layer to access data stored in different data stores. Multiple data adaptors may be included in the data management layer. These adaptors can access multiple data sources and retrieve different types of data. Data adaptor platform 116 can access multiple data sources over a network. A network can be either a local area network, a large network like a wide-area network (WAN), or an ensemble of networks such as the Internet. Data adaptor platform 116 can use multiple connectors. These connectors could be applications, APIs and protocols. They are used to provide connectivity with data sources or data stores. (JMS), Apache Kafka?, Apache Flume?, Apache Solr?, Java Database Connectivity? (JDBC), User Datagram Protocol, (UDP), and/or such

Accordingly, the data adaptor 116 may supply the network fix app 118 with data from various data sources. The data store 117 stores the data. Data in the datastore 117 is accessible to the network repair application 118. The illustrated embodiment may contain a trouble ticket datasource 110, an operationdatasource 111, an alert datasource 112, and other data sources.

The trouble ticket data source 110 could contain data about issues with components or operations of wireless carrier networks. Software agents that monitor the performance and health of the wireless carrier network may automatically generate network trouble tickets. Customers and/or customer service representatives may manually enter subscriber trouble tickets to describe problems they are experiencing. Data on administrators and resolution reports may be included in trouble ticket data source 110. Statistics on each type of issue reported, as well as statistics on issues resolution rates.

The operation data source 112 may contain a data collection that contains performance information about the wireless network and user devices using it. In different embodiments, performance information could include Radio Access Network (RAN), OSS counters and Call Detail Records (CDRs), VoLTE calls traces, VoLTE call traces and Session Initiation Protocol [RTP] Control Protocol (RTCP trace data, user devices data traffic logs, system event logs, bug reports and/or other information about the network and device components. The data collection may further provide network topology data, network expansion/modification data, network coverage data, and planned maintenance data. Network topology data can include the locations of network cell, network backhauls, core components, and so forth. Information about the network coverage may include information about signal coverage and communication bandwidth capabilities, performance specifications, operation statuses, backhaul, network cell, and core components of backhaul and network cells, as well as data on network coverage. These network cells could include macrocells and picocells as well as femtocells and microcells.

The operation data source 111 may also provide performance information for user devices. This information could include account and user device information. Subscribers on the wireless carrier network may have access to device information that includes technical capabilities, features, operational status, and other details. Multiple subscribers may have different account information, including billing preferences, subscriptions to service plans, payment histories, data consumption statistics, etc.

The alarm data source 112 could include alerts for wireless carrier networks that are generated based upon predetermined alert rules by status monitoring applications of the network. An alert rule could specify that an alert should be sent when certain conditions are met. These conditions could include specific faults or issues with network components, deviation of predetermined threshold performance levels, complaints from users about network components, network nodes, or network services reaching or failing to meet a predetermined threshold and/or other such things.

The social media data supply 113 could include data collected from social networking portals. An evaluation of the network fix application (118) may determine that a wireless carrier network can establish a social networking portal. A third-party service provider may also maintain a social networking portal for users to post social media posts. Another social networking portal could be one that is maintained solely by a user for their social posts. Social networking portals allow users to share and comment on information, reviews, and/or comments about service providers, products and services, merchants and networks, and so forth. Social networking portals can include blogs, message feed web pages and web forums. An individual user can create a customized social networking portal to allow other users to subscribe to their social postings, leave comments, or perform other social networking activities. These social postings can highlight problems in the network that wireless carriers have encountered by subscribers from different geolocations.

A data mining algorithm from the data adaptor platform 112 may be used to extract words, phrases, quotes, and ratings relevant to the operating conditions or performance status of nodes, components, or services of the wireless carrier network. Data mining algorithms can use machine learning as well as non-machine techniques, such as association rule learning, decision tree learning and artificial neural networks. They may also use inductive logic, support vector machines (SVMs), clustering and Bayesian networks to extract patterns. One example is that the data adaptor platform (116) may find a pattern in web blog posts that indicate users are unhappy with a certain aspect of the service offered by a wireless carrier network at a specific location. Another example is that the data adaptor platform 112 may detect a pattern in message feed postings by multiple users. This could indicate that a particular type of user device has an extremely high error rate when it is used with the wireless carrier network.

The additional data source 114 could include data from third-parties or wireless carriers, such as reports from network monitoring tools. The network monitoring tools can include configuration tools, optimization tools and diagnostic tools. Key performance indicators (KPIs) may be included in data reports. KPI configuration files may be used to generate KPIs. The KPIs can measure the performance of specific devices or network components. The KPIs can also be used to provide aggregated performance measurements of multiple device or network components for specific classes or networks, and/or other such information.

In certain embodiments, the network repair application 118 includes a root cause analysis module (119), recommendation module 120, as well as an action tracker (121). The network fix app 118 can analyze wireless carrier network performance using multiple data sources obtained by the data adaptor plate 116. It can provide one or more predicted root causes 115 via root cause analysis module 112 and prioritize network fixes 124 via recommendation module 120 for each identified quality of service problem. The network fix application 118 also has the ability to verify network performance improvements via the action tracker121 after the network fix is implemented.

The root cause analysis module (119) can provide a prediction of root cause 115 for a problem related to one or more performance data. This is to assist in the resolution quality of service issues for wireless carriers and to alert 123 to the probable root cause (e.g. to an administrator, network engineer, and/or administrative entity). The root cause analysis module (119) can be used to identify common points of failure as well as areas where problems are occurring based on KPIs. The predicted root cause module, 119 can then match the performance pattern to a previously stored performance pattern or symptom that corresponds with a specific type of data transmission problem and/or root causes.

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