Analytics Excellence

This success story illustrates our commitment to leveraging data analytics to overcome operational challenges, drive efficiency, and elevate client satisfaction within the Financial Services sector

Summary of Challenge

A medium-sized Financial Services company encountered challenges in assessing the productivity of their KYC (Know Your Customer) team. Despite a focus on throughput, inefficiencies within their process hindered their capacity to boost and sustain productivity levels. 

Solution Design

Key Tools Used: Alteryx & Power BI

A custom data analytics solution was designed using Alteryx to extract and transform data from internal servers. The transformed data would then be loaded onto a Power BI dashboard consisting of visuals highlighting team performance through average throughput times, KYC pipelines, and error rates post-KYC completion.

Results

Bottleneck Identification

Stakeholders were able to identify multiple bottlenecks with the KYC process with the help of average completion time graphs for each stage of the KYC process. Changes to the process increased throughput by 23%.

Error Rate Reduction

Team metrics were analysed to understand where error rates were most prevalent. The areas were identified and trainings were provided to team members reducing error rates by 47%.

Transparency and Workload Distribution

The dashboard was rolled out to team members to provide greater transparency on the pipeline. Stakeholders were then able to ensure KYC cases were assigned accordingly to ensure the workload was evenly distributed. This further reduced error rates by 14% and increased throughput by 23%.

Improved Client Satisfaction

A byproduct of the changes saw client satisfaction increase by 32% as the process required less back and forth through emails while maintaining a higher KYC throughput.

This success story showcases how automating regular reports for an Investment Management firm drastically reduced production time, enhanced productivity, and mitigated operational risks.

Summary of Challenge

Investment Management firms face complex challenges, especially in Investment Operations. One firm struggled with time-consuming MI (Management Information) reporting, requiring collaboration across global teams and manual data extraction from information systems and spreadsheets. These delays not only hindered efficiency but also posed potential operational and regulatory risks.

Solution Design

Key Tools Used: Python & Power BI

The devised solution involved employing a combination of Python and Power BI for data extraction. Python was integrated within the Power BI environment to extract data from systems requiring API connectivity. Additionally, databases such as MongoDB, Denodo, and Microsoft Excel were accessed using Power BI functionality. Data transformation within Power BI was conducted to generate requisite views akin to the original report. These encompassed critical insights such as failed trades, corporate actions, liquidity profiles, and high-level portfolio overviews. 

Results

Rapid Reporting

The amalgamation of analytical tools led to a 75% reduction in report production time. This time-saving aspect was further amplified across all stakeholders involved, as less time was required for data analysis and synthesis. 

Increased Productivity

Teams were provided access to the report, facilitating the tracking of relevant metrics and providing heightened visibility into operational processes. The real-time nature of the report empowered senior stakeholders to holistically assess their functions, enabling the design of targeted solutions aimed at bolstering productivity. 

This success story highlights how Python can be used to develop a sophisticated revenue analytics system, resulting in significant cost savings, proactive risk mitigation, and versatile insights for a small Brokerage business facing challenges with suspicious trading activity. 

Summary of Challenge

Within a small brokerage business, there were increasing levels of suspicious trading activity where individuals would take advantage of lagging price updates for currency pairs using robotic trading systems. These systems would identify lagging price updates between stock exchanges and the business which was not only costly for the firm but had serious regulatory and reputation implications.

Solution Design

Key Tool Used: Python

The solution centered around Python, where a sophisticated revenue analytics system was developed and tailored to the organization's needs. Leveraging Python's advanced data processing capabilities, trade data was extracted and analyzed to identify anomalies indicative of suspicious activity. This included assessing the region where the trade originated, the length of time a position was held, and the daily fluctuations in account balance. The analysed report would automatically disseminate this information with associated graphics by email to stakeholders for prompt action.

Results

Enhanced Cost Saving

The implementation of the suspicious trading report provided stakeholders with enhanced visibility into accounts exhibiting irregularities. This facilitated the identification of suspicious traders, resulting in an average daily cost saving of £8,000. 

Proactive Pre-trade Identification

Teams successfully discerned patterns in account opening behaviors, aiding in the identification of potentially suspicious individuals. This proactive approach led to a 35% reduction in suspicious trading activity, as onboarding teams enforced stringent verification processes before granting trading privileges. 

Report Versatility and Reusability

Beyond its primary function, the report proved versatile in assessing client portfolios. It not only identified suspicious accounts but also provided insights into the trading behaviors of non-suspicious clients. This enabled the firm to ensure liquidity was plentiful and changes in client portfolios were adjusted in hedged accounts for both Institutional and Retail clients.