{"id":50620,"date":"2026-05-21T23:19:57","date_gmt":"2026-05-21T20:19:57","guid":{"rendered":"https:\/\/mk.gen.tr\/ai-adoption-in-mortgage-servicing-is-accelerating-blackwolf-says\/"},"modified":"2026-05-21T23:19:57","modified_gmt":"2026-05-21T20:19:57","slug":"ai-adoption-in-mortgage-servicing-is-accelerating-blackwolf-says","status":"publish","type":"post","link":"https:\/\/mk.gen.tr\/tr\/ai-adoption-in-mortgage-servicing-is-accelerating-blackwolf-says\/","title":{"rendered":"AI adoption in mortgage servicing is accelerating, BlackWolf says"},"content":{"rendered":"

A new white paper from BlackWolf Advisory Group<\/strong> says adoption of artificial intelligence<\/a> in mortgage servicing<\/a> is accelerating as firms seek to reduce costs, improve compliance and automate routine tasks.<\/p>\n

While the paper says servicers are ready and actively adopting AI, regulatory scrutiny and legacy technology systems remain as major barriers.<\/p>\n

The paper, titled \u201cMortgage Servicer\u2019s Strategic Guide to AI Adoption,\u201d said AI adoption among mortgage lenders more than doubled between 2023 and 2025, rising from 15% to 38% of companies. Servicers using AI<\/a> are reporting 30% to 50% reductions in customer service costs and 25% to 40% decreases in document processing expenses, the report noted.<\/p>\n

Mirza Hodzic, managing director and founder of BlackWolf, said that since servicers\u2019 needs vary, it can be hard to pinpoint when and where each entity should start incorporating AI into workflows.<\/p>\n

\u201cNo servicer is the same when it comes to the way they do business. \u2026 They have different data, they have different budgets and sizes, so when we look at the overall thought process and things that the servicers are looking at, they don\u2019t know where to get started,\u201d Hodzic told HousingWire<\/strong>. <\/p>\n

\u201cWhat we do is a foundational assessment of readiness to implement AI, and that\u2019s generally to understand what the big needs at the moment are, what [their] technology looks like, current capabilities, and where [they] want to be six months, one year or further down the road.\u201d<\/p>\n

BlackWolf\u2019s paper cited several examples of AI already in use across the mortgage industry. It said a pilot program at BSI Financial Services<\/a><\/strong> achieved a 73% resolution rate for AI-handled inquiries and reduced weekly calls to human agents by 400. Rocket Mortgage<\/a><\/strong> was also cited for processing more than 1.5 million documents per month using generative AI tools that automatically classify forms and extract data fields.<\/p>\n

The report said predictive AI models are helping servicers identify borrowers at risk of default earlier, while fraud detection tools are shortening review timelines from weeks to seconds. It noted that Fannie Mae<\/strong> partnered with<\/a> Palantir Technologies<\/strong> on fraud<\/a> detection technology that can identify suspicious activity in about 10 seconds, compared with a traditional 60-day investigative process.<\/p>\n

BlackWolf said most mortgage companies are relying on third-party vendors<\/a> rather than building proprietary AI systems. The report cited a 2025 STRATMOR Group<\/a><\/strong> survey showing 63% of lenders using AI rely on vendor solutions, while only 20% are developing custom tools internally.<\/p>\n

\u201cYou can\u2019t just rely on a vendor and say, \u2018Well, the vendor is the AI tool.\u2019 You\u2019re responsible for it,\u201d Hodzic said. \u201cRight now, buying is the option people choose when deciding whether to build or buy AI, [but] when you build it, you\u2019re investing in it a bit longer term because you\u2019re going to own the risk fully.\u201d<\/p>\n

Major servicing technology providers are embedding AI directly into their platforms, according to the report. ICE Mortgage Technology<\/strong>, whose MSP platform<\/a> serves more than 100 clients, has integrated AI-powered automation agents into servicing workflows. Meanwhile, Sagent<\/strong> has added AI tools for document automation and predictive analytics to its Dara platform<\/a>.<\/p>\n

At the same time, the report warned that regulatory oversight of AI in mortgage servicing is intensifying. It highlighted new governance requirements<\/a> from Freddie Mac<\/strong> that took effect in March 2026 and require servicers to implement formal AI governance policies, security assessments and executive oversight.<\/p>\n

Organizational resistance remains one of the biggest obstacles to AI adoption. BlackWolf cited research from McKinsey & Co.<\/strong> showing 32% of respondents expect workforce reductions<\/a> where AI is deployed.<\/p>\n

The white paper also alluded to servicer errors when deciding to implement AI functionalities.<\/p>\n

\u201cMistakes we\u2019ve seen include jumping headfirst into AI without a plan, just to keep up with maybe some other competitors,\u201d Hodzic said. \u201cIt takes a while. It takes experts in the field to understand and implement it correctly. Just throwing money and time at it, or asking your internal team to handle all of it, is putting them in a tough spot.\u201d<\/p>","protected":false},"excerpt":{"rendered":"

A new white paper from BlackWolf Advisory Group says adoption of artificial intelligence in mortgage servicing is accelerating as firms seek to reduce costs, improve compliance and automate routine tasks. While the paper says servicers are ready and actively adopting AI, regulatory scrutiny and legacy technology systems remain as major barriers. The paper, titled \u201cMortgage…<\/p>","protected":false},"author":0,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false},"categories":[1],"tags":[],"_links":{"self":[{"href":"https:\/\/mk.gen.tr\/tr\/wp-json\/wp\/v2\/posts\/50620"}],"collection":[{"href":"https:\/\/mk.gen.tr\/tr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mk.gen.tr\/tr\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/mk.gen.tr\/tr\/wp-json\/wp\/v2\/comments?post=50620"}],"version-history":[{"count":0,"href":"https:\/\/mk.gen.tr\/tr\/wp-json\/wp\/v2\/posts\/50620\/revisions"}],"wp:attachment":[{"href":"https:\/\/mk.gen.tr\/tr\/wp-json\/wp\/v2\/media?parent=50620"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mk.gen.tr\/tr\/wp-json\/wp\/v2\/categories?post=50620"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mk.gen.tr\/tr\/wp-json\/wp\/v2\/tags?post=50620"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}