Financial services firms struggle with modernizing their legacy technology platforms. These platforms holds most, if not all, of the critical data in these institutions. Given these platforms have been built over the years, the data is fragmented across the enterprise with no coherent data strategy around curation, translatio, integration, storage, quality governance compliance. Therefore, these institutions spend most of their transformation budget on solving data challenges and focusing very little on real business needs.
All of this can result in fragmented platforms across asset classes and geographies. These barriers often prevent even the best-intentioned firms from (a) attaining increased automation levels, (b) performing platform consolidation (due to lack of a “golden source” of data), and (c) transforming IT to operate in a modern environment.
As a result, some financial firms are struggling to achieve greater delivery speed (e.g., faster client onboarding, enhanced participant experiences), increase efficiency (e.g., access to real-time pretrade insights, such as compliance checks), and successfully meet regulatory and compliance deadlines.
Firms need pragmatic “data initiatives” (which include both data strategy and data integration initiatives) to help them overcome these barriers and transform their operations through technology investments in order to increase profitability.
All of this can result in fragmented platforms across asset classes and geographies. These barriers often prevent even the best-intentioned firms from (a) attaining increased automation levels, (b) performing platform consolidation (due to lack of a “golden source” of data), and (c) transforming IT to operate in a modern environment.
As a result, some financial firms are struggling to achieve greater delivery speed (e.g., faster client onboarding, enhanced participant experiences), increase efficiency (e.g., access to real-time pretrade insights, such as compliance checks), and successfully meet regulatory and compliance deadlines.
Firms need pragmatic “data initiatives” (which include both data strategy and data integration initiatives) to help them overcome these barriers and transform their operations through technology investments in order to increase profitability.
FlowML enable you to address business needs through Data - making real transformation easier.
Successful legacy technology modernisation invariably depends on the ability to harness the power of data to achieve operational excellence, comply with regulatory requirements and enable valuable insights for your business whilst simultaneously provide a superior client experience.
What if you could:
* Seamlessly support new business requirements without a need for costly replacement of existing platforms?
* Improve scalability and automations using existing solutions?
* Run and grow the business faster using AI and real time customer analytical insights?
* Address data access rights and data quality issues without having to change legacy code?
* Introduce predictive digital monitoring solutions and controls also to your existing platforms?
What if you could:
* Seamlessly support new business requirements without a need for costly replacement of existing platforms?
* Improve scalability and automations using existing solutions?
* Run and grow the business faster using AI and real time customer analytical insights?
* Address data access rights and data quality issues without having to change legacy code?
* Introduce predictive digital monitoring solutions and controls also to your existing platforms?
Ability to attract and retain top technical talent
Most financial firms operate at a huge scale with complex global operations and a high legacy IT footprint. This often translates to the unavailability of high-quality, easily accessible data for new technology solutions. Data complexity, coupled with a stringent regulatory regime, buries firms under operational issues and leaves them with very little appetite for innovation. Thus, firms constantly play catch-up when it comes to adopting new technology and keeps having to use their technology resources to fix legacy issues.
Today’s top technical talent do not wish to work on legacy systems and platforms because they correctly assume that it could limit their ability to develop transferable skills in a very competitive job market. Since traditional financial firms are still burdened with many legacy platforms, they may find it difficult to attract the right talent to help them thrive and compete with new and more tech-savvy competitors.
FlowML helps bridge that by moving most of the activity away from the backend and onto modern platforms which are attractive and which provides for career growth and learning for the future.
Today’s top technical talent do not wish to work on legacy systems and platforms because they correctly assume that it could limit their ability to develop transferable skills in a very competitive job market. Since traditional financial firms are still burdened with many legacy platforms, they may find it difficult to attract the right talent to help them thrive and compete with new and more tech-savvy competitors.
FlowML helps bridge that by moving most of the activity away from the backend and onto modern platforms which are attractive and which provides for career growth and learning for the future.