Why AI-Powered Enterprise Search for Financial Services?

Today, operational productivity in fintech depends more than ever on the up-to-dateness and transparency of shared enterprise knowledge. Yet, preventable delays still occur in teams’ work. When employees can’t verify with enterprise knowledge bases timely, many interconnected processes slow down or even halt.

It frustrates, it hurts inner culture, and it might let down clients of Support. So, let’s see what the limitations of legacy technologies are and how conversational AI tools can challenge them.

Key Challenges of Unifying Internal Knowledge

For several reasons, accessing and acting upon necessary enterprise knowledge has become more difficult lately. Full-remote operations in financial services and fintech induced major issues that blew up traditional work communication and onboarding approaches.

Remote First Culture

Today, trainees and new teammates of globally distributed teams are mostly left to their own devices when they get to new tasks. Sure thing, there are daily meetings and briefs, but those might easily turn into pointless time and productivity killers.

That said, companies can fix that by enabling straightforward access to shared tools and knowledge bases. Therefore, they will benefit from technological upgrades and preserve the simplicity of communication between distributed team members.

Poorly Supervised Onboarding

The remote-first culture also hurt quality control in the onboarding process. The trainees and new hires are mostly given access to Notion or Jira knowledge bases; they connect to dedicated Slack channels, and that’s it. They’re up to themselves during their training period.

No wonder the productivity of such unsupervised onboarding is low.

Answer Delays and Distractors

Slack and other communication channels get cluttered with replies pretty fast. So it might take a while for colleagues to respond.

However, it’s crystal clear that such answers might be brief and incomplete. Moreover, even the “quick” question distracts your colleagues who have work to do. Implementing a smart enterprise search solution can fix these issues and eliminate delays and distractors.

Lack of Context Behind Current Operations

Lastly, understanding particular work tasks and processes is often incomplete for the reasons mentioned above. Even experienced employees might miss out on the context of the conclusions and outcomes of business activities if they didn’t follow them from the beginning.

Rich context added to the requested information will foster an in-depth understanding of work objectives.

Why Is Keyword Search No Longer an Option?

The legacy keyword search technology has become unproductive due to serious limitations:

  • Siloed Data Repositories. The tech stacks of fintech companies currently consist of dozens of external services and internal applications. Even though they have a native search feature, users must look for subject-related materials and documentation separately.
  • Low Relevance of the Result.Enterprise search based on indexed meta-data may return with unsatisfactory results. The system might match your request with files and records related to completely different use cases.

The Benefits of AI-Powered Search Assistants

Fintech and tech companies started adopting AI search assistants not so long ago. However, these automation tools have fundamentally changed enterprise search. AI search assistants provide:

  • A Single Interface.Almost every business application and software has its native keyword search. But users don’t have time to scrape through each of them – they need an instant result with a clear reference to a certain case, project, or document. And conversational search AI can deliver it.
  • Visibility and Accessibility of Knowledge Base. Supervisors and team leaders should no longer bother themselves with providing access to requested documentation. An AI assistant retrieves necessary information lightning-fast and keeps users updated on every recent change in business documentation.
  • Understandable Prompts. Natural language processing allows users to conversate with chatbots freely and get answers that are meaningful to them. For instance, the user can ask the chatbot about additional details on the client’s application for a new credit limit, and it will provide historical information on the latest borrowings and debt settlements.
  • Deeply Contextual Search Results. Search assistants can logically group and organize data by understanding its use patterns and interconnections between disparate apps.
  • AI Assistant Learns From Your Activities. Therefore, it can predict what you’re looking for and read your intent.
  • Various Integration Options. AI search systems integrate with external business applications and services quickly and seamlessly. They don’t change the existing data ecosystems of external and internal business apps and don’t require dataset pre-indexing.

Privacy and Security Concerns of AI-Powered Enterprise Search

Sensitive data safety is taken seriously by trusted AI tech providers. However, the COO and other chief executives must be 100% certain about privacy compliance fulfilled by providers.

The robust data protection is backed up by:

  • SOC-2 type 2 compliance
  • GDPR certification approved by independent auditors
  • The regular penetration tests.
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