The Five Most Relevant IoT Use Cases in Financial Services for 2021
Let’s explore the most relevant IoT use cases in financial services in 2021. There are now more than 30 billion IoT devices streaming data to machine learning, BI and analytics apps. Much of this activity originates in financial transactions like account transaction logs, mortgage payments, stock trades, credit scores, and fraud analysis. Those are the obvious sources. But there are many more varied types of data that are being collected, such as the accelerometer in your phone that is used to detect anomalies in your vital signs and behavior correlated with deception patterns which have the ability to report predictions to your bank’s fraud department.
While the model is trained from streaming IoT sensors, your phone is also streaming data for real-time fraud detection. New streaming ML apps detect both regular and irregular behavior. In this short blog we will discuss those IoT use cases that are most critical to the financial industry. The majority of ML streaming apps are for making money, but many new applications have the related goal to prevent losing money.
With petabytes of valuable data flowing into data warehouses from IoT computer vision, facial and speech recognition sensors, performance and reliability are increasingly valued and mandated by the executive team. The most performance and reliable enterprise messaging offering on the market today is Apache Pulsar. Real-time event streaming now automatically updates AI and machine learning models which are essential to the accuracy of five of the most common and important financial forecasting models:
- Credit risk assessment
- Fraud detection
- Investment portfolio management
- Bank promotional marketing performance forecasting
- Financial systems security
The diversity of data types and formats generated by IoT networks requires the use of a robust stream processing and messaging platform. Apache Pulsar’s native architecture was designed for this task. Pulsar is better than its predecessor, Apache Kafka, at decoupling applications; it was also designed with integration architecture in mind, so that the components are easily pluggable. Pulsar likewise excels as the messaging component in the Enterprise Service Bus (ESB) architecture, which is also essential in many large scale ML applications. Consequently, Pulsar is a logical solution choice for the most complex event streaming ML use cases in the financial industry space.
To show how this works, we will illustrate several IoT streaming ML use cases which are important today in the financial services sector. We will also lay out a case for why a managed Pulsar solution provides a competitive edge in financial business intelligence.
First, a Look at General IoT Use Cases…
Before exploring IoT use cases specific to finance, let’s briefly review Big Data from IoT overall. The particulars of IoT stream processing in finance will then have a sharper frame of reference. Generally, architectures for IoT use cases traverse a broad spectrum.
Edge computing is important to reduce the volume of data transmitted from IoT to Cloud processing, and as we shall see, the calculation intensiveness of financial forecasting demands that we optimize data preparation and Edge Compute modeling. Briefly, Edge compute methods seek to do as much data prep and pre-processing as possible before transmitting anything to Cloud. In many use cases, Edge Compute generates insights faster.
Hybrid architecture, as the name implies, mixes a variety of IoT data protocols and formats, such as JSON and XML, and streams them through pub/sub platforms with both open source and proprietary components, ultimately to query engines like Presto and other analytics solutions which may be white-labeled, API, SOP, or microservices. Hybrid architectures also include a combination of Cloud and on-premise commodity hardware distributed across multiple regions. In these types of environments a robust middleware with native geo-replication and multi-tenancy are critical to ensure successful implementation. These are the qualities which make Pulsar an ideal fit for hybrid IoT architectures.
Streaming machine learning Applications are optimized by Edge compute designs in which IoT data sources feed ML models directly; then, only the prediction outcomes must be sent to Cloud applications, such as BI dashboards and other analytics. This is the fastest solution and also reduces overhead in the messaging platform. An ideal solution includes an open source analytics and machine learning engine like PandioML combined with Trino and Presto, which are optimized for financial forecasting applications. Pandio is also an expert partner in managed streaming services.
Bringing all this new tech together without sacrificing data security and integrity is a point of competitive differentiation for an enterprise streaming solution like Apache Pulsar. How exactly do we set up IoT device streaming to ML apps for financial services forecasting?
We can configure Apache Pulsar stream processing to trigger an SQL query when data published or streamed to a topic meets criteria. ML models are therefore immediately updated with the most recent data from IoT devices, phones, program traders, transaction logs, ATM machines, lab and factory monitors, and many more. The outcome is data that is already prepared for ML models and updating those models in real time, at the instant of streaming. Today this is popularly called “data in motion.” Let’s look at how Data in Motion works in the financial services industry.
Event Streaming for Dynamic Modeling in Finance Use Cases
The best methods and technologies in every component of the system are absolutely necessary to achieve competitive success in financial forecasting and portfolio management. For the five broad use cases mentioned in the intro, let’s pair each with a real-world application IoT application:
- Concept drift detection framework – Credit risk assessment, fraud detection, and data security
- Wearable IoT Sensors and Fraud Detection ML
- Long Short Term Memory (LSTM) – Investment portfolio management
- Recurrent Neural Nets (RNN) – Marketing promo campaign forecasting
- AI Driven Chatbots Streaming Customer Interests to ML
- IoT Streaming Finance Data and Concept Drift
An advanced and highly beneficial machine learning method for three of our five finance use cases is called “Concept Drift.” Briefly, concept drift describes a long term pattern in existing ML models which accumulate forecasting errors from increasingly historical data. In a nutshell, concept drift recognizes patterns in errors; it’s like ML that learns from ML.
In a common scenario, a new IoT network or hub coming online suddenly introduces new dynamics which render existing prediction models obsolete. One of the causes is known as concept drift. Now, we have models which were trained in the past with data which is conceptually supplanted by a new wave of IoT source data. In principle, in the field of predictive analytics and machine learning, concept drift occurs because the statistical properties of a target variable – in this use case the client’s credit score which the ML model is trying to predict – varies or drifts over time in unimagined ways. Paradoxically, ML can also be used now to predict concept drift. Drift detection as well as drift adaptation strategies are now built into ML models to achieve greater accuracy. Credit risk assessment frameworks thus now include such methods for dynamic credit scoring based on IoT streaming. Drift in fraud detection and data security models likewise benefit from using one ML to detect and correct errors in another ML.
- IoT Wearable Sensors and Fraud Detection Analytics
The related financial fields of risk assessment, fraud detection, and data security, are all optimized with IoT streaming ML applications of anomaly detection methods – and in most interesting and surprising ways! One system under development collects pulse and respiration data to sense changes in a user’s physiology which correlate with deception.
The proliferation of sensor device types combined with real-time analytics of data streamed from IoT via Apache Pulsar is a phenomenon for data security. Deep neural nets can now learn regular and irregular patterns to enable predictive analytics for automated notification to fraud department team members and directly enhance decision support. The neural network effectively learns an irregular human activity pattern from streaming IoT data emerging from wearable sensors used to train models. Then, phone accelerometer data from users shows a 98.75 percent accuracy at anomaly detection in physical activity monitoring.
- IoT Streaming ML in Investment Portfolio Management Today
Current research in the improvement of investment decision-making systems intends to optimize efficiency and stabilize investment returns. One effort under way today seeks to solve a widespread design problem in traditional decision support systems, wherein database, model, and knowledgebase often evolved independently from existing or legacy systems; whereas an ideal design would be natively integrated and organic, instead of a synthesis. The most efficient system under design today is an IoT platform streaming live data via pub/sub like Pulsar to an ML decision support system. An important innovative component of this new system is the creation of a market-facing data mining model to dynamically analyze and promptly propose investment decisions.
Sensing equipment in the IoT platform under design represents an insight that the data to be collected and streamed will provide an intelligence in real-time that will be the new standard of instantaneous intelligence gathering. The sensors will include wearable devices intended to collect data directly from human actors. Of course, the usual data will be extracted, such as trades and transactions for time series analysis. However, more invasive human vital sign anomaly and pattern detection is proposed to identify deception and stress, even to capture discussion keywords. In terms of the physical technology, terminals are proposed within the IoT platform network to establish seamless IoT link communication, which is highly optimized by Apache Pulsar, and especially as a managed service with Pandio as host. Pandio expert ML partners operate the ideal managed Pulsar solution for building a new IoT sensor platform for financial intelligence and decision support.
- IoT in Marketing Promo Campaign Forecasting
The use of recurrent neural net methods in modeling customer behavioral patterns and anomalies is well researched and promising. Such pattern recognition algorithms can successfully predict promotional success based on demographic modeling data. Banking and finance promotional campaigns use these algorithms for relevancy-based recommendations, computational linguistics in SEO apps, ROI optimization, and many other use cases.
In fact, financial instrument marketing campaigns are among the top use cases for AI because the behavioral trends targeted by recurrent neural nets tend to be more accurate for personality types involved in investment and assets.
- AI Driven IoT Chatbot Agents Stream Critical Customer Finance Data
An exciting new area of customer tech support in auto traders and online program trading is the AI powered chatbots which can answer many customer support queries without referral to human agents. Chatbot models are trained by expert domain knowledge data, and reportedly get better customer reviews than human counterparts.
Now, the next step in to use chatbot customer dialogs to capture keywords and phrases for use in BI dashboards and analytics. As chatbots improve, the IoT use cases for streaming their conversations with customers via IoT through a Pulsar ESB channel are many and exciting.
AI chatbots with Edge compute capability may recognize trends in regional client interest by capturing keywords related to investment interest, and then automatically refer customers to financial advisors, while simultaneously building recommendations for those customers who express particular interests. ML can then operate quiescently to build intelligent campaign strategies to fit those customer’s needs.
Expert Partners in IoT for Finance
We have seen that IoT sensors of many types are now capable of gathering and analyzing critical finance data at the source. From IoT platform to Cloud based ML algorithms which feed BI and decision support, one important component seamlessly integrates these applications: Apache Pulsar. As and ESB middleware solution, Pulsar outperforms all other pub/sub apps.
Pulsar is the most secure and reliable, guaranteeing zero data loss and the lowest latency in the industry. For financial organizations seeking to optimize an IoT streaming application, Pandio’s managed service for IoT streaming applications provides a secure and reliable experience. In addition, Pandio’s end-to-end AI Orchestration, enabling enterprises to build robust data pipelines at scale, provides enterprises with a way to quickly deploy and drive AI initiatives into production.