Enabling Analytics with the Finxact CaaS

The recent trends in machine learning, artificial intelligence (AI), and analytics have shown that the market is adopting the concepts at rapid speed. Industries have been shifting from a “nice to have” outlook to a “it is a necessity” viewpoint when it comes to an analytics-driven enterprise. Recent initial public offerings (IPOs) from the likes of analytics-focused companies Snowflake, MongoDB, Palantir, Exasol, and Sumo Logic are indications of the growth in usage and popularity these companies are seeing. The big three cloud providers Microsoft Azure, Google Cloud Platform, and Amazon Web Services are providing a constantly expanding suite of analytics and machine learning-focused services to users and businesses, with new capabilities being added each day. With this mass proliferation of new technology, keeping up can seem impossible.

Analytics has shifted away from the old ways of tabular spreadsheets, monolithic databases, and “green bar” reports. Data is being produced and consumed in volumes and velocities higher than ever previously imagined, with the upper limit being pushed further each minute. According to International Data Corporation (IDC), 59 zettabytes of data will be generated this year, and the data created and consumed over the next three years will be greater than the last 30 years combined. Taking into consideration these staggering amounts, it is hard to imagine relying on days’ past methods for analysis.

FINXACT CAAS

Finxact, as the first real-time next-generation core-as-a-service, was born into a world that understands the importance of enabling enterprises to consume data as quickly as it is produced. Legacy core systems rely on nightly batch processes, extract routines, and monolithic data tables that slow down “time-to-action” and limit the capabilities of an organization’s analytics initiatives.

The Finxact CaaS provides multiple features that enable real-time access to all operational data, as well as the ability to read records at a point in time in the past. These features combined enable a financial institution to build a fully integrated analytics solution that can drive business decisions and respond to changes at a moment’s notice. By leveraging events produced by the Finxact CaaS and accessing the fully transparent APIs, an organization is able to have full unbound access to the data on their system-of-record. In addition to this, the temporal qualities of the Finxact CaaS online data storage means that institutions can “go back in time” to rebuild data sets and source the data from a point in the past. Let’s look at some use cases where this power is realized.

RELATIONSHIP PROFITABILITY

An institution’s ability to know their customers, and the contribution those customers make to their revenue, is tantamount to their ability to retain and nurture customer relationships. One way an institution can gain insight into their customer base is by analyzing relationship profitability. Relationship profitability analysis requires a vast amount of data spanning from single transactions to entire portfolio relationships. On a traditional system, these data points may be obfuscated away or hidden deep in a batch-produced report somewhere. This data should be able to be accessed at a moment’s notice so that action can be taken “now” rather than “tomorrow using yesterday’s data.” The Finxact CaaS provides full access to all underlying operational data in real time via its everexpanding suite of API endpoints.

Organizations can integrate bleeding-edge solutions such as serverless computation and managed ETL systems to read from the Finxact CaaS APIs and populate data storage solutions such as data lakes, data warehouses, and data marts on any schedule they please. This flexibility and online access to all data on the system-of-record means that organizations can perform complex tasks such as analyzing relationship profitability at any time they wish.

REAL-TIME FRAUD PREVENTION

The Finxact CaaS, in addition to the expansive transparent API endpoints, also produces real-time events that enable event-driven architectures and responsive analytics pipelines. Traditional core systems are essentially “black box” solutions that consume tons of data each day, but hamper an organization’s ability to extract and consume the data, let alone in real-time. By integrating with the event-driven offering that Finxact provides, organizations can consume
real-time events to turn key areas such as business process management, fraud prevention, and anti-money laundering (AML) into responsive cost-saving powerhouses. Gone are the days of batch reports, bulky extracts, and waiting for nightly processing to finish in hopes of retrieving the data. The Finxact events architecture means data can be consumed as quickly as it is generated.

Applied to fraud prevention, Finxact’s offering is second to none when it comes to responsiveness and ease-of-integration. Taking into consideration the billions lost each year to fraud, being able to respond as quickly as possible
to situations is critical to an organization’s ability to mitigate risk. The event architecture that Finxact brings forth makes it easy for third party fraud management systems to integrate with real-time transaction data and customer information from the system-of-record. This real-time integration means that fraud can be stopped dead in its tracks before it’s too late.

TREND ANALYSIS AND TIME-BASED REPORTING

The Finxact CaaS maintains temporal records, meaning that the system can be read from at a point in time in the past. This means that organizations can supply their data warehouses, data lakes, data marts, and any other systems
with historical data, even if the system was integrated at a later date. By having this capability, organizations can generate historical reports, trending data analysis, and detailed time-based reporting, without having to worry if they have been collecting the dataset in its entirety from inception. If an organization finds that there is value in a particular data element that they had not been consuming, the organization can then extract the entire history of that data element to populate downstream systems. In addition to this, the lineage of the data extract the entire history of that data can be validated by correlating record versions, timestamps, and primary keys to the system-of-record.