Financial services institutions must have efficient internal operations to stay competitive and increase profitability, particularly as fintech and tech companies make further inroads into the financial services space. In theory, there are two ways to improve profitability: increase revenue and decrease expenses. Both are critical. To address the challenge of growing profitability in an increasingly fast-moving sector, financial services institutions are renewing their focus on using the vast amount of data and information available to help them improve operational efficiency and performance. By taking a data-driven approach to streamlining processes, eliminating redundancies, and optimizing resource allocation, financial services organizations can both reduce costs and improve their service delivery.
This last factor is especially important in such a highly competitive, rapidly evolving, and continuously disrupted industry. Organizations that operate efficiently can offer more competitive pricing, faster and superior service, improved accuracy, and better customer experiences than their competitors. Customers appreciate quick and hassle-free experiences, and positive customer experiences contribute to customer loyalty, retention, and positive word of mouth, all of which can help drive growth and revenue.
Operational efficiency also provides the foundation for agility and adaptability, helping financial services organizations stay ahead and respond quickly to market changes, regulatory requirements, and customer demands. Agile organizations can launch new products faster, adapt to technology advancements, seize emerging opportunities, and thrive in a dynamic environment.
Additionally, operational efficiency plays an important role in effective risk management, which is critical for maintaining the trust and confidence of customers, regulators, and stakeholders. Operational inefficiencies, including manual errors, process bottlenecks, and inadequate controls, can introduce risks and lead to compliance violations, security breaches, and operational disruptions. By improving operational efficiency, financial services organizations can help mitigate these risks, ensure regulatory compliance, and enhance the security of customer data.
These benefits are all essential for growth. As financial services organizations expand their operations or enter new markets, they need to ensure their processes can handle increased volumes without sacrificing quality or incurring excessive costs. Efficient processes can be easily replicated, automated, or adapted to support growth initiatives, enabling organizations to seize opportunities and expand their market presence.
By ingesting, curating, and analyzing data on operational processes and performance, financial services organizations can identify and eliminate bottlenecks and inefficiencies to optimize every internal and external interaction and improve outcomes. The architecture presented here demonstrates how we can combine recommended Oracle components to build an analytics architecture that covers the entire data analytics lifecycle and is designed to help financial services institutions achieve the wide range of business benefits described above.
Our solution is composed of three pillars, each supporting specific data platform capabilities. The first pillar provides the capability to connect, ingest, and transform data.
There are four main ways to inject data into an architecture to enable financial services organizations to enhance operational efficiency and performance.
Data persistence and processing is built on three components. Some customers will use all of them, others a subset. Depending on the volumes and data types, data could be loaded into object storage or loaded directly into a structured relational database for persistent storage. When we anticipate applying data science capabilities, then data retrieved from data sources in its raw form (as an unprocessed native file or extract) is more typically captured and loaded from transactional systems into cloud storage.
The ability to analyze, learn, and predict is facilitated by two technology approaches.
Advanced analytics capabilities are critical for optimizing operational efficiency and performance. In this use case, we rely on Oracle Analytics Cloud to deliver analytics and visualizations. This enables the organization to use descriptive analytics (describes current trends with histograms and charts), predictive analytics (predicts future events, identifies trends, and determines the probability of uncertain outcomes), and prescriptive analytics (proposes suitable actions to support optimal decision-making).
Applying predictive models to historical data, financial service organizations can forecast future outcomes and make proactive decisions. For instance, predictive analytics can help banks anticipate customer churn, identify potential fraud cases, predict credit defaults, and optimize cash flow forecasting. This allows banks to take preventive actions and allocate their operational resources effectively.
Prescriptive analytics goes beyond predicting outcomes and provides recommendations on the best course of action. Financial services organizations can use prescriptive analytics to optimize decision-making in areas such as loan approvals, investment strategies, pricing models, and risk management. By considering various constraints and objectives, prescriptive analytics helps organizations make data-driven decisions that maximize efficiency and profitability. (The broader data culture in an organization will ultimately play a significant role in the success of a predictive analytics approach.)
In addition to advanced analytics, increasingly data science, machine learning, and artificial intelligence are used to look for anomalies, predict where process latency might occur, and optimize the customer journey. For example, machine learning models can be used for credit scoring, fraud detection, customer segmentation, and personalized marketing. By continuously learning from new data, these models can adapt and enhance their performance over time, leading to increased operational efficiency and better decision-making. OCI Data Science, OCI AI Services, and Oracle Machine Learning can be used in the databases.
We use machine learning and data science methods to build and train our predictive models. These machine learning models can then be deployed for scoring via APIs or embedded as part of the OCI GoldenGate stream analytics pipeline. In some cases, these models can even be deployed in the database using the Oracle Machine Learning Services REST API (to do this, the model needs to be in Open Neural Network Exchange format). Additionally, OCI Data Science for Jupyter/Python-centric notebooks or Oracle Machine Learning for the Zeppelin notebook and machine learning algorithms can be deployed within the serving or transactional data store. Similarly, Oracle Machine Learning and OCI Data Science, either alone or in combination, can develop recommendation/decision models. These models can be deployed as a service, and we can deploy them behind OCI API Gateway to be delivered as “data products” and services. Finally, once built, the machine learning models can be deployed into applications that are part of an operational decisioning system (if permitted).
As the speed of business—and the level of competition—increases, legacy systems used to deliver critical operational data can’t keep up. These systems need a lot of manual intervention to collate, integrate, and create reports from fragmented and siloed data, and that means the information arrives too late to give the business the advantage it needs. Measuring, understanding, and improving operational efficiency can provide financial services organizations with a competitive advantage and numerous benefits, including the following:
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