Feature
Artificial intelligence (AI) is changing the way financial services companies are operating. Financial firms are implementing AI to solve many of their tech issues, such as enabling new business, document processing and regulatory compliance. The following case studies provide some examples of how different firms in the financial industry are applying AI technologies:
1. Mogo
Mogo, one of Canada’s leading digital finance companies, needed a way to properly identify politically exposed persons (PEPs) due to the broad definitions provided by different regulators. Another challenge was that those definitions can change, challenging finance companies to stay abreast of current descriptions, according to a case study.
There is no universal PEP list, and outdated or issue-specific lists make the identification process time-consuming and error prone.
Mogo was dissatisfied with the performance of its previous service provider, which relied on outdated keyword matching, limited data and great manual effort. The PEP coverage was incomplete and didn’t have the depth required to meet regulatory requirements. Mogo, then, switched to Minerva's AI-powered screening and entity resolution platform.
The platform “adapts quickly to changes in sanctions and other regulatory requirements, providing us with a high level of confidence in their services,” says a senior director of governance and risk at MOGO.
Results
- Identified over 5% more PEPs than a global solution and 40% more than a Canadian-based solution
- Reconciled the PEP population within existing client base, which is equivalent to 1% of the base
- Expanded integration to assess risks related to trading products, including regulator disciplinary and cease trade order lists from industry organizations
2. FFAM360
FFAM360, which provides business process outsourcing (BPO) and accounts receivable management for bank and retail debt, medical debt and other verticals, wanted to help its managers and agents work more efficiently and effectively without adding to their non-call activities and with little additional training, according to a case study.
The company sought to reduce the amount of time agents spent on after-call work and help agents meet key compliance requirements and repayment goals, while also improving the compliance review process.
FFAM360 chose Prodigal’s suite of AI-powered solutions, starting with the implementation of ProInsight, which analyzes calls and automatically flags portions for follow-up. Prodigal’s ProNotes uses AI and machine learning (ML) to generate call notes. Prodigal’s ProAssist provides agents with real-time direction during calls.
“We've seen a jump in payments and an increase in other critical agent effectiveness areas, like compliance scores, rebuttals and objections,” says Paul Allen, CEO, FFAM 360.
Results
- 90% jump in speed of compliance and call reviews
- 25% increase in agent effectiveness
- 12% increase in payment discussions
See more: 10 Top AI Courses for Finance Pros
3. Consult Venture Partners
Consult Venture Partners wanted to differentiate itself from others in the wealth management market by offering online interactions via chat when many others relied on human agents, according to a case study.
To provide automated online assistance, the company looked for an enterprise-grade digital assistant that could respond to website visitors’ questions like a human help desk agent, not a typical rules-based chatbot. Alda Ai, a conversational AI digital concierge from IBM partner INATIGO, fit its needs. Alda Ai uses IBM watsonx assistant technology to understand questions posed in natural language. The solution offers security and scalability provided through IBM Cloud.
The firm also created an efficient way to engage with as many people as possible from the moment they reached the firm’s website. The automated assistant provides detailed answers to questions regarding the firm’s products and services, guides users to the right pages and suggests that they sign-up for a webinar or personal consultation.
“It was very important that if we were going to be setting a precedent in pioneering this type of technology in our firm, we had to be sure that this would be one of the top AI assistant experiences in the industry,” says Ashlea Atigolo, managing partner, Consult Venture Partners.
Results
- Successfully answered 92% of queries correctly
- Webinar registrations resulted from 47% of queries
- 39% of inquiries turned into leads
4. PPS
PPS, a South African-based insurance company, wanted to transform itself from a traditional broker-based business into a digital insurance provider. A key pillar of the new strategy was to overhaul the company's technology infrastructure, according to a case study.
The digital changes included using an AI platform built on the open-source ML platform TensorFlow by Google and Google Cloud ML Engine, which enabled engineers to host and build ML models. Previously, it took one to three months to manually build a model and match an offer to a customer. With the ML tools and models, a match took a few minutes. Cloud ML Engine supported adapting to new information and making adjustments to preset variables that define the model-training process.
PPS deployed its new AI recommendation platform, and a couple of months later, its impact was clear.
"In around eight weeks, we saw a 5% growth in sales," says Avsharn Bachoo, CTO, PPS. "It's been a direct result of building our recommendation platform. … We can offer the right products to the right members."
PPS’ move to the cloud, including containerized Kubernetes development with Google GKE, allowed PPS engineers to work in mature testing environments and build pre-production environments.
“Our servers were at the end of their life cycle, and we had to decide whether to refresh them or switch completely,” says Avsharn Bachoo, CTO, PPS. “To embrace the world of AI and machine learning effectively, we knew we needed a cloud-based infrastructure.”
Results
- Improved sales by 5%
- Ran 70% faster than on-premises infrastructure with fewer resources
- Added flexibility to strategic goals with manageable operational expenditure flows
5. Indecomm
Indecomm, provider of the GeniusWorks suite that automates back-office mortgage operations, looked to use machine learning for its data extraction solution, Intelligent Document Extraction (IDX), according to a case study.
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IDX goes beyond optical character recognition (OCR) to identify and classify documents as well as extract, validate and certify data and enrich data as needed. The automated data extraction cuts significant time needed for reading, analyzing and comparing information across a large repository of documents. Machine learning could reduce time further, making the process much faster for lenders, meaning faster decisions, processing and loan closings.
Indecomm implemented Amazon Textract to automate complex document review and extract data from images and text for analysis. Clients using IDX can halve the time required for underwriting and mortgage origination, ensuring data accuracy.
The ML-based document tool gave the company “the highest flexibility of use and lowest cost to develop our IDX module,” says Harish B. Kamath, SVP of engineering, Indecomm.
Results
- Data classification and extraction time reduced to five to seven minutes
- 50%-60% less manual document intervention required
- Automated scaling
See more: 5 AI Case Studies in Banking
