Improve financial services operations and performance

Grow profitability in financial services with data-driven operational efficiency

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.

Optimize operational efficiency and lower costs with a comprehensive data platform

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.

This image shows how Oracle Data Platform for financial services can be used to support and improve operational efficiency and performance. The platform includes the following five pillars:

1. 1. Data Sources, Discovery
2. 2. Ingest, Transform
3. 3. Persist, Curate, Create
4. 4. Analyze, Learn, Predict
5. 5. Measure, Act

The Data Sources, Discovery pillar includes three categories of data.

1. 1. Oracle App’s data comprises data from Fusion SaaS, Oracle E-Business Suite, CX
2. 2. Business Records (1st Party Data) CRM, Transactions, Account Information, Revenue, and Margin
3. 3. 3rd Party Data comprises Forex Rates, Market Feeds, and Commodity Prices

The Ingest, Transform pillar comprises four capabilities.

1. 1. Batch ingestion uses OCI Data Integration, Oracle Data Integrator, and DB tools.
2. 2. Bulk transfer uses OCI FastConnect, OCI Data Transfer, MFT, and OCI CLI.
3. 3. Change data capture uses OCI GoldenGate.
4. 4. Streaming ingest uses OCI Streaming Kafka Connect.

All four capabilities connect unidirectionally into the serving data store and cloud storage within the Persist, Curate, Create pillar.

Additionally, streaming ingest is connected to stream processing within the Analyze, Learn, Predict pillar.

The Persist, Curate, Create pillar comprises five capabilities.

1. 1. The serving data store uses Oracle Autonomous Data Warehouse and Exadata Cloud Service.
2. 2. Cloud storage uses OCI Object Storage.
3. 3. Managed Hadoop uses Oracle Big Data Service.
4. 4. Batch processing uses OCI Data Flow.
5. 5. Governance uses OCI Data Catalog.

These capabilities are connected within the pillar. Cloud storage is unidirectionally connected to the serving data store; it is also bidirectionally connected to batch processing.

Two capabilities connect into the Analyze, Learn, Predict pillar. The serving data store connects to both the analytics and visualization capability and also to the data products, APIs capability. Cloud storage connects to the machine learning capability.

The Analyze, Learn, Predict pillar comprises two capabilities.

1. 1. Analytics and visualization uses Oracle Analytics Cloud, GraphStudio, and ISVs.
2. 2. Machine learning uses Oracle Machine Learning.

The Measure, Act pillar captures how the data analysis may be used: by people and partners.

Peoples and Partners comprises Operational Efficiency (Processing times, Error rates, Resource utilization), Process Bottleneck Identification, Customer Lifetime Value, Market and Competitive Analysis, Performance Attribution.

The three central pillars—Ingest, Transform; Persist, Curate, Create; and Analyze, Learn, Predict—are supported by infrastructure, network, security, and IAM.

Connect, ingest, and transform data

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.

• To start our process, we’ll enable the bulk transfer of operational transaction data. Bulk transfer services are used in situations where large volumes of data need to be moved to Oracle Cloud Infrastructure (OCI) for the first time—for example, data from existing on-premises analytic repositories or other cloud sources. The specific bulk transfer service we’ll use will depend on the location of the data and the transfer frequency. For example, we may use OCI Data Transfer service or OCI Data Transfer Appliance to load a large volume of on-premises data from historical planning or data warehouse repositories. When large volumes of data must be moved on an ongoing basis, we recommend using OCI FastConnect, which provides a high-bandwidth, dedicated private network connection between a customer’s data center and OCI.
• Frequent real-time or near real-time extracts are commonly required, and data is regularly ingested from transaction, and customer management systems using OCI GoldenGate. OCI GoldenGate uses change data capture to detect change events in the underlying structure of the systems that deliver the operational processes that need to be serviced (for example, the creation of an account, a customer issue, fraud indications, and so on) and sends the data in real time to a persistence layer and/or the streaming layer.
• The ability to analyze data from multiple sources in real time can help provide financial services organizations with valuable insights into their operational efficiency and overall performance so they can understand and measure the efficiency of their core processes. In this use case, we use streaming ingest to ingest all the data read from customer or internal events through mobile interactions, IoT, machine-to-machine communications, and other means. Streams can originate from a variety of internal and external sources and can include transaction data, customer interaction data, market data, social media data, and data from compliance and regulatory systems. Data (events) will be ingested, and some basic transformations/aggregations will occur before it is stored in OCI Object Storage. Additional streaming analytics can be used to identify correlating events and any identified patterns can be fed back (manually) for an examination of the raw data using OCI Data Science.
• While real-time needs are evolving, the most common extract from transactional, enterprise resource planning, customer, and risk and compliance management systems is some kind of batch ingestion using an ETL process. Batch ingestion is used to import data from systems that can’t support data streaming (for example, older mainframe core banking systems). These extracts can be ingested frequently, as often as every 10 or 15 minutes, but they are still batch in nature as groups of transactions are extracted and processed rather than individual transactions. OCI offers different services to handle batch ingestion, such as the native OCI Data Integration service and Oracle Data Integrator running on an OCI Compute instance. The choice of service would primarily be based on customer preference rather than technical requirements.

Persist, process, and curate data

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.

• Cloud storage is the most common data persistence layer for our data platform. It can be used for both structured and unstructured data. OCI Object Storage, OCI Data Flow, and Oracle Autonomous Data Warehouse are the basic building blocks. Data retrieved from data sources in its raw format is captured and loaded into OCI Object Storage. OCI Object Storage is the primary data persistence tier, and Spark in OCI Data Flow is the primary batch processing engine. Batch processing involves several activities, including basic noise treatment, missing data management, and filtering based on defined outbound datasets. Results are written back to various layers of object storage or to a persistent relational repository based on the processing needed and the data types used.
• We’ll now use a serving data store to persist our curated data in an optimized form for query performance and provide a 360-degree view of the organization’s operations. The serving data store provides a persistent relational tier used to serve high-quality curated data directly to end users via SQL-based tools. In this solution, Oracle Autonomous Data Warehouse is instantiated as the serving data store for the enterprise data warehouse and, if required, more-specialized domain-level data marts. It can also be the data source for data science projects or the repository required for Oracle Machine Learning. The serving data store may take one of several forms, including Oracle MySQL HeatWave, Oracle Database Exadata Cloud Service, or Oracle Exadata Cloud@Customer.

Analyze data, learn, and predict

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).

• The final yet critical component is data governance. This will be delivered by OCI Data Catalog, a free service providing data governance and metadata management (for both technical and business metadata) for all the data sources in the data platform ecosystem. OCI Data Catalog is also a critical component for queries from Oracle Autonomous Data Warehouse to OCI Object Storage as it provides a way to quickly locate data regardless of its storage method. This allows end users, developers, and data scientists to use a common access language (SQL) across all the persisted data stores in the architecture.

The benefits of using data to enhance operational efficiency and performance

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:

• An improved ability to acquire and retain customers through efficient service delivery, competitive pricing, a superior customer experience, and innovative offerings
• Better business decisions, informed by a single and consistent view of accurate data that’s available at the right time
• Better agility, allowing organizations to launch new products faster, adapt to technology advancements, and seize emerging opportunities
• Reduced complexity across the organization
• Reduced data duplication and manual errors
• Decreased risk through improved risk management and mitigation
• Reduced costs
• Faster data availability for analytical purposes