Enable Cross Functional Banking Surveillance
Pull Together a Holistic and Preventative Approach
Perpetrators of financial crime don’t refer to a playbook of best practices to execute their schemes. They are resourceful, creative, and collaborative in their approach to obtain their objectives and avoid getting caught.
Regulators are aware of this and expect that you take a proactive and holistic approach to prevent (not just identify) financial crime. They enforce this by punishing both the crime and your procedural failures to catch it. The market penalizes surveillance failures through large losses by rogue traders, while public opinion drives corporate embarrassment due to personal misconduct.
To prevent regulatory, financial, and reputational risk, it is not enough to flag events. You also need to understand the behaviors of those involved, to reveal their intent. The volume and variety of data needed to pull together that holistic view requires you to move beyond traditional data platforms and business intelligence tools so that your surveillance program is robust, defensible, and financially sustainable.
- Show real-time and historical data analysis side by side in one application.
- Analyze all types of data in place, without moving it, in an ad hoc manner.
- Alert designated people based on real-time data.
- Integrate machine learning for accelerated data evaluation and modeling.
- Visually blend data that enables cross-reference and correlation across various data sources.
- Build real-time data apps for critical metrics as well as visuals that simplify time-series analysis.
- Securely share models with internal and external constituents.
- Allow compliance officers to do this in a self-service fashion.
Improve the Economics of the Strategy
Best-of-breed trade surveillance platforms, artificial intelligence (AI), and advanced analytics generate data that flag and evaluate risks buried deep within daily trading volume and communications. However, the cost, time, and personnel resources needed to store, process, and correlate that data across business functions and surveillance channels are prohibitive. Improving the economics of a strategy is a key factor in the program’s success.
Modern big data environments such as data lakes process more varieties and higher volumes of data at a significantly lower cost than traditional data platforms. Native visual analytics and anti-fraud software increase the return on investment by enabling your team to drive the surveillance process in a timely, secure, and collaborative way.
Add geographic data to understand global risk.
Explore data to understand overlapping client relationships (“who is who” and “who owns whom”).
Keys to a Holistic Banking Surveillance Program
Cross-functional collaboration among subject matter experts prevents financial crime. A holistic approach to preventing financial crime is executed through a combination of human intuition, subject matter expertise, data processing, and advanced anti-fraud software. Mitigating regulatory, financial, and reputational risk starts with an analytics/BI platform that drives the key directives of your trade surveillance strategy.
Example Arcadia Data Capabilities
|Let subject matter experts drive the process — not IT. Compliance officers, business analysts, and their teams are best equipped to identify unusual behaviors based on their internal business knowledge and market expertise. They understand the business flows and can quickly discern nuanced disruptions that could indicate a risk.|| Self-service tools that let them work on their own. Example capabilities in Arcadia Data include: |
|Leverage all data, big or small. The subject matter experts must have the ability to explore and evaluate any type of data (structured, unstructured, real-time, or historical). Discovery is key to spotting and investigating potential risks, so they need to be able to easily explore data.||Support for all data types. Correlate structured data, textual data (with search interface), streaming data (with pause and play), complex data types (arrays, maps, structs), combined real-time + historical data as/when needed to explore or test a theory.|
|Ensure timeliness and data integrity. Crime is pervasive and ever evolving. To accelerate an informed decision-making process and stay ahead of the game, it is critical to ensure the integrity of the underlying data and to optimize the models that use it.||No data movement to a separate BI server. All data and processing (including machine learning and AI) is directly accessible in the data lake and in its native format. Time-to-insight is accelerated because there are no delays from data transformation overhead. Data integrity is ensured because the data remains in its existing form with no disruptive ETL operations on it, and teams collaborate on the same version.|
|Enable Collaboration The efficiency and efficacy of your team’s work increases through collaboration, leveraging the collective intellect of colleagues who can provide more insight into transactions, product details, and communications to provide direct and expert feedback.|| Governed workspaces. Let colleagues share workspaces while maintaining security with granular access controls and column-level security on sensitive data. |
Embeddability. Integrate into custom web apps with trusted authentication to securely share models with internal and external constituents and investigators.
Problem: Fragmented Surveillance
Analysts work in silos, limiting the opportunities to spot sophisticated risk activities that cross-functional areas.
Solution: Collaborative Surveillance
Teams leverage each other’s expertise, sharing analytic views on all data to make faster decisions and proactively address risk exposures.
Develop a Robust and Defensible Process, One Step at a Time
Experts (your peers) advise a measured and iterative approach to demonstrating a robust and defensible surveillance program to regulators: a) start with low hanging fruit; b) make it a collaborative effort; c) leverage public data sets; and d) expand gradually from there. Arcadia Data enables your teams to derive business value at any stage of maturity within your big data strategy, from exploring multiple data stores, to leveraging streaming data and advanced analytics, to optimizing how you share insights across your enterprise
Inform decision making with advanced analytics by correlating transactions patterns with business networks and product details.
- Ingest the data. The information you depend on will be coming to you via an array of live data streams and in large batches.
- Store the data. Huge volumes and high varieties of data from disparate sources will need to be combined and stored.
- Explore the data. Enable subject matter experts to correlate conventional and any type of alternative data types to flesh out ideas.
- Operationalize. Collaborate with colleagues across business functions to test and put your strategies into operation.
- Leverage streaming data and advanced analytics. Gain deeper and timely insight with streaming data and advanced analytics.
- Optimize. Improve the economics of your strategy by optimizing how you distribute data insights among teams and clients.
Connect Investigators with Big Data Capabilities
Visually analyze all types of data at a granular level from within the data lake, leveraging the native formats of both alternative and conventional data sets so your teams can collaborate on the same big picture. They will have confidence in the integrity of the analysis because the data hasn’t been translated (i.e., summarized/aggregated and then moved to a separate visualization server).