In today’s Financial Industry, performance of application is one of the most critical aspects. Whether it is a banking application dealing with millions worth of transactions, balance transfers or trading application dealing with time sensitive trades and orders or Customer care portal, dealing with clients’ accounts or secure payment processing, these applications must always work efficiently.
A delay for even milliseconds or break down disruptions can result in huge trading loses, failed payment transactions and ultimately losing customer trust leading to financial and reputational damage to these institutions. Therefore, there is simply no place for failures, errors or any other form of performance degradation factors in financial applications.
As these financial applications grow more complex, they must be optimized for better speed, reliability, efficiency and stability while ensuring security. Achieving this manually is quite challenging and time consuming, therefore many financial institutions are leaning towards AI and Machine to optimize their applications.
Performance testing is conducted to evaluate how an application or software system performs under various conditions such as speed, load, stress, stability, efficiency and reliability.
The Goal of the performance testing is to improve the application’s performance, thus resulting in improvement of business, time, money and its user’s efficiency, as all these factors are interrelated. Based on this testing, the speed, failures, scalability and other issues are fixed and optimized.
Traditional performance testing tools generally provides static analysis and metrics, but AI powered tools go much more beyond that. They offer predictive analysis using real time data as feed, automated root cause analysis, code optimization techniques, auto failure detection and self-learning models, thus merging intelligent automation and real time learning into performance testing.
There are different types of Machine algorithms used to achieve predictive performance optimization. Some of them are:
A Random Forest is an ensemble averaging machine learning model algorithm. This algorithm is based on “decision trees”. Each tree output is independent, but at the end the average voting result of all the trees output in the forest is taken as a final result.
ï ŷ = predicted response time or system load.
ï D = number of decision trees.
ï 𝒙 = data point of the input data and θ is independent and identically distributed outputs of a randomizing variable
By training this model using historical data, it provides predictive analysis i.e., predicts application future slowdowns and allocates resources accordingly to prevent performance degradation.
Bayesian optimization algorithms are generally used to automatically adjust the parameters of the web front-end framework. It is a probabilistic model-based optimization method. It approximates the objective function by constructing a surrogate model and then selects the optimal parameter combination based on the surrogate model. It usually performs well in handling high-dimensional, non-convex, performance optimization problems in web front-end frameworks.
Bayesian Optimization is based on Gaussian Processes (GP) to model an unknown function f(x) and decide where to sample next.
Acquisition function (Expected Improvement - EI), which decides the next sample point:
By adjusting the configurations automatically, it reduces latency and increases speed during peak hours.
These algorithms are used for anomaly detection based on the patterns there by detecting the system failures early before it gets escalated further.
The Architecture of the AI driven performance optimization framework is designed to enhance the traditional performance optimization tools by introducing intelligent automation and efficient optimization and fine tuning. To achieve that the first step is to identify the application Test scenarios.
They should be defined in a well-structured format. The outline should be very clear with configuration details including number of users, load duration, UI related requests like JS or Json or API requests, cache usage, retry mechanisms. In most cases, test scenarios are taken on transaction basis. For example, in a banking application, the main functionality is to enter the credentials and Login, then search for an Account and finally see the Account Summary details.
For that test scenario, each module can be divided into transactions. Login to Homepage as Transaction T1, Search for Account as Transaction T2, Account details as Transaction T3.
Once simulation scripts for all the transactions are ready, execute them with the test executor, which simulates the interactions against system under test. During the execution, the system is put under artificial stress under controlled load depending upon the test scenarios to observe the behavior of the application.
As the tests run, Metric Collector captures the metrics like latency percentiles at different levels. Example: P65, P75, P95. Other metrics also includes Median, Standard Deviation Time, error rates, throughput, Timeouts, CPU utilization, Memory consumption and other response times.
AI engine is the intelligence core layer of this framework. The metrics received from the test executor is collected and used to feed and train the models of Random forests for latency prediction, Bayesian optimization model for configuration tuning and Support vector Machines for failure detection. Based on this predictive analysis the AI engine identifies performance bottlenecks, future load issue’s, configuration scaling, changes to parameters like cache, thread pool size and other parameters.
The Optimizer module gets the recommendations from AI engine and updates the configuration settings. Once set these are moved to test re-executor component, where it reruns the tests with new improved configuration settings, there by creating a feedback loop, executing the tests and refining and fine tuning the next ones, allowing the system to adapt the enhancements continuously. Thus, the framework keeps evolving the performance of application, growing accurate with each cycle.
This AI driven framework works as All-in-One performance optimization package giving insights on hosts, pods level metrics, logs and supports technologies to display metrics on dashboards to view results real time without waiting till the run completes and eases out API, UI requests performance optimization and continuous delivery pipelines. This framework also allows to automate the functional tests and reuse them as performance tests every time when there is a code change.
This framework is also highly cost efficient. With AI predicting loads and adjusting allocated resources dynamically, it automates cloud scaling and distributes transactions with smart load balancing and improving database query optimization thus reducing compute costs.
Conclusion: The Future of AI in Financial Performance Optimization
AI driven performance optimization framework is an innovative solution for complex financial software applications, by providing real time predictive analytics, enhanced speed, response times, reliability and self-optimizing infrastructure and much more.
By using machine learning models like Random forests, Bayesian optimization and Support vector machine algorithms, this architecture stands as not just an upgrade but as future for financial companies seeking efficient, faster and resilient high stakes applications where every millisecond counts.