With COVID-19, it has become clear that ideas and assumptions that worked in the past will no longer apply now and in the future. The region is expected to face its deepest economic slowdown since the 1970s due to COVID-19. Businesses might be wondering what to do next.
Deep learning has evolved in the past five years from an academic research domain, to being adopted, integrated and leveraged for new dimensions of productivity across multiple industries and use cases, such as medical imaging, surveillance, IoT, chatbots, robotic,s and many more. From NLP to computer vision, deep learning has been breaking the barriers of SOTA algorithms and providing results that were, otherwise, impossible to achieve.
Fraud awareness in the Financial Services industry is more important than ever. According to the September 2020 benchmarking report conducted by the Association of Certified Fraud Examiners (ACFE) in response to the coronavirus, 77% of survey respondents, representing a range of industries, have observed an increase in the overall level of fraud as of August, compared with 68% in May. The report reveals 68% of respondents have observed an increase in payment fraud schemes (vs.
We’re honored to win this prestigious award, and we’re doubling down on the Lakehouse architecture with Databricks SQL analytics plans. Fivetran is the proud recipient of the Databricks ISV Partners Innovation Award as announced at this week’s Data & AI Summit Europe event. The award recognizes how Fivetran has collaborated with Databricks to empower data professionals to accelerate time to insights with Delta Lake and the Lakehouse architecture for the modern data stack.
One of the many areas where machine learning has made a large difference for enterprise business is in the ability to make accurate predictions in the realm of fraud detection. Knowing that a transaction is fraudulent is a critical requirement for financial services companies, but knowing that a transaction that was flagged by a rules-based system as fraudulent is a valid transaction, can be equally important.
The success and growth of companies can be determined by the technologies they rely on in their tech stack. To deploy AI enabled applications to production, companies have discovered that they’ll need an army of developers, data engineers, DevOps practitioners and data scientists to manage Kubeflow — but do they really? Much of the complexity involved in delivering data intensive products to production comes from the workflow between different organizational and technology silos.
