Banking

Today, banks are under siege from a horde of fintech companies who are more nimble than the large financial institutions. But the big banks have deeper expertise and more data, and they can win by harnessing their data and leveraging the power of artificial intelligence (AI) and machine learning to reduce costs, increase revenues, and more efficiently comply with regulations.

Syncrasy’s Data Discovery Platform provides a powerful enterprise-ready data integration, curation, data warehousing, visualization and analytics platform to underpin AI initiatives.

DataRobot’s Automated Machine Learning platform empowers existing business teams to solve analytic problems rapidly. From prospecting new corporate customers to more nimble model risk management, the banks that leverage automated machine learning will be the ones that survive the Fintech invasion.

Syncrasy, DataRobot AI and Banking

As an ever increasing number of fintech companies make an already competitive market even more so, banks are being forced to look for ways to improve the effectiveness and efficiency of their business. Syncrasy and DataRobot AI helps banks improve their bottom line.

Banking Use Cases

Banks are looking to use machine learning to increase top and bottom line through gaining competitive advantages, reducing expenses, improve efficiencies and optimizing all areas of their business from risk analysis and fraud detection to marketing.

View all Banking Use Cases

Data Aggregation & Curation Pipelines

Whether real-time streaming or batch ingest, Syncrasy helps Banks cost effectively connect to, transform, curate, enhance and analyse all data types, sizes and volumes – at speed. Driving 360º views of Customers and the Enterprise as a whole. Creating new insights and generating new value.

Modern Data Warehousing

Syncrasy’s Hyperconverged Datawarehouse helps Banks cost effectively store all of their data, whatever the data types, sizes and volumes. Leveraging Hadoop and Kubernetes technologies, Syncrasy’s Datawarehouse provides high-availability and planet-scalability on low cost COTs hardware combined with tightly integrated data security down to database cell level and data masking capability on ingest and reporting, ensuring Banks have the right tools to implement GDPR compliance requirements covering management of Personal Identifiable Information.

Risk Management

Syncrasy and DataRobot AI helps risk teams better manage exposure to many types of risk – from credit risk and market risk to model risk and regulatory risk. With applications ranging from anti-money laundering (AML) and Know Your Customer (KYC) to fraud detection and prevention to model risk management and credit risk forecasting, Syncrasy and DataRobot helps Chief Risk Officers and their risk teams maximize their resources and deliver better risk protection.

Wholesale Banking

Treasury and Cash Management

Retail Banking

Special Assets / Workout

Special assets teams can use Syncrasy and DataRobot AI to determine the most efficient, highest value approaches to managing delinquent loans. Whether it’s prioritizing and allocating resources optimally, delivering better loan valuations for recoverability and cost models, or valuing portfolios for securitization and secondary markets, Syncrasy and DataRobot offers the tools and speed for your special assets teams.

Merchant Services

Cards

Mortgage Companies

From managing interest rate and prepayment risk to improving collections, AI helps mortgage companies and services operate more efficiently and profitably. Applications where Syncrasy and DataRobot excels include predicting prepayment speeds and refinancing likelihood, as well as early warning detection for delinquencies and prioritizing collections to make the best use of your outbound callers’ time.

Investment Banking

Trading & Markets

Asset Management & Alternatives

Wealth Management

Customer retention represents a substantial challenge for most wealth managers, but by using AI, wealth managers can identify the best new clients and provide early warning about potential customer attrition. Syncrasy and DataRobot helps wealth managers with use cases ranging from new customer prospecting to customer attrition to relationship deepening. Syncrasy and DataRobot also offers the tools for wealth manager to developer their own robo-advisor offerings.

The tip of the iceberg

Machine Learning and Artificial Intelligence technologies are only “the tip of the data processing iceberg”.

The deep substance is what is below the water line.

Before Machine Learning and Artificial Intelligence technologies can usefully be deployed, enterprises need to access, curate and store all of their internal and external data sources, including previously disregarded data.

Unfortunately, for most of large enterprises meaningful raw data tends to reside in line of business systems across multiple business units, and often with overlapping and conflicting information. In addition, legacy databases, ETL and storage systems are not technologically consistent with Machine Learning and Artificial Intelligence technologies due to an explosion of data types and volumes, as well as the commercial requirement for actions based on real-time information.

To be able to think about deploying Machine Learning and Artificial Intelligence enterprises must first establish “the deep substance” - the underlying databases and applications capable of delivering the data to Machine Learning and Artificial Intelligence processes together with the computing and storage architectures to support it.

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